Merge branch 'master' into cc/summarization_patch

# Conflicts:
#	libs/langchain_v1/langchain/agents/middleware/summarization.py

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -8,16 +8,15 @@ body:
       value: |
         Thank you for taking the time to file a bug report.
 
-        Use this to report BUGS in LangChain. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
 
-        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
-        if there's another way to solve your problem:
+        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
 
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference](https://reference.langchain.com/python/),
+        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
         * [LangChain ChatBot](https://chat.langchain.com/)
         * [GitHub search](https://github.com/langchain-ai/langchain),
+        * [LangChain Forum](https://forum.langchain.com/),
   - type: checkboxes
     id: checks
     attributes:
@@ -36,16 +35,48 @@ body:
           required: true
         - label: This is not related to the langchain-community package.
           required: true
-        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
-          required: true
         - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
           required: true
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Which `langchain` package(s) is this bug related to? Select at least one.
+
+        Note that if the package you are reporting for is not listed here, it is not in this repository (e.g. `langchain-google-genai` is in [`langchain-ai/langchain-google`](https://github.com/langchain-ai/langchain-google/)).
+
+        Please report issues for other packages to their respective repositories.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
   - type: textarea
     id: reproduction
     validations:
       required: true
     attributes:
-      label: Example Code
+      label: Example Code (Python)
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
 
@@ -53,15 +84,12 @@ body:
 
         **Important!**
 
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
-        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
+        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        * Reduce your code to the minimum required to reproduce the issue if possible.
 
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: python
       placeholder: |
-        The following code:
-
-        ```python
         from langchain_core.runnables import RunnableLambda
 
         def bad_code(inputs) -> int:
@@ -69,17 +97,14 @@ body:
 
           chain = RunnableLambda(bad_code)
           chain.invoke('Hello!')
-        ```
   - type: textarea
-    id: error
-    validations:
-      required: false
     attributes:
       label: Error Message and Stack Trace (if applicable)
       description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
+        If you are reporting an error, please copy and paste the full error message and
+        stack trace.
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: shell
   - type: textarea
     id: description
     attributes:
@@ -99,9 +124,7 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us. Do NOT skip this step and please don't trim
-        the output. Most users don't include enough information here and it makes it harder
-        for us to help you.
+        Please share your system info with us.
 
         Run the following command in your terminal and paste the output here:
 
@@ -113,8 +136,6 @@ body:
         from langchain_core import sys_info
         sys_info.print_sys_info()
         ```
-
-        alternatively, put the entire output of `pip freeze` here.
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +1,18 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: 📚 Documentation
+  - name: 📚 Documentation issue
     url: https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml
     about: Report an issue related to the LangChain documentation
   - name: 💬 LangChain Forum
     url:  https://forum.langchain.com/
     about: General community discussions and support
+  - name: 📚 LangChain Documentation
+    url: https://docs.langchain.com/oss/python/langchain/overview
+    about: View the official LangChain documentation
+  - name: 📚 API Reference Documentation
+    url: https://reference.langchain.com/python/
+    about: View the official LangChain API reference documentation
+  - name: 💬 LangChain Forum
+    url: https://forum.langchain.com/
+    about: Ask questions and get help from the community

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -13,11 +13,11 @@ body:
         Relevant links to check before filing a feature request to see if your request has already been made or
         if there's another way to achieve what you want:
 
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference](https://reference.langchain.com/python/),
+        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
         * [LangChain ChatBot](https://chat.langchain.com/)
         * [GitHub search](https://github.com/langchain-ai/langchain),
+        * [LangChain Forum](https://forum.langchain.com/),
   - type: checkboxes
     id: checks
     attributes:
@@ -34,6 +34,40 @@ body:
           required: true
         - label: This is not related to the langchain-community package.
           required: true
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Which `langchain` package(s) is this request related to? Select at least one.
+
+        Note that if the package you are requesting for is not listed here, it is not in this repository (e.g. `langchain-google-genai` is in `langchain-ai/langchain`).
+
+        Please submit feature requests for other packages to their respective repositories.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
   - type: textarea
     id: feature-description
     validations:

.github/ISSUE_TEMPLATE/privileged.yml

@@ -18,3 +18,33 @@ body:
     attributes:
       label: Issue Content
       description: Add the content of the issue here.
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Please select package(s) that this issue is related to.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general

.github/ISSUE_TEMPLATE/task.yml

@@ -25,13 +25,13 @@ body:
       label: Task Description
       description: |
         Provide a clear and detailed description of the task.
-        
+
         What needs to be done? Be specific about the scope and requirements.
       placeholder: |
         This task involves...
-        
+
         The goal is to...
-        
+
         Specific requirements:
         - ...
         - ...
@@ -43,7 +43,7 @@ body:
       label: Acceptance Criteria
       description: |
         Define the criteria that must be met for this task to be considered complete.
-        
+
         What are the specific deliverables or outcomes expected?
       placeholder: |
         This task will be complete when:
@@ -58,15 +58,15 @@ body:
       label: Context and Background
       description: |
         Provide any relevant context, background information, or links to related issues/PRs.
-        
+
         Why is this task needed? What problem does it solve?
       placeholder: |
         Background:
         - ...
-        
+
         Related issues/PRs:
         - #...
-        
+
         Additional context:
         - ...
     validations:
@@ -77,15 +77,45 @@ body:
       label: Dependencies
       description: |
         List any dependencies or blockers for this task.
-        
+
         Are there other tasks, issues, or external factors that need to be completed first?
       placeholder: |
         This task depends on:
         - [ ] Issue #...
         - [ ] PR #...
         - [ ] External dependency: ...
-        
+
         Blocked by:
         - ...
     validations:
       required: false
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Please select package(s) that this task is related to.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,28 +1,30 @@
 (Replace this entire block of text)
 
-Thank you for contributing to LangChain! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
+Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview
+
+Thank you for contributing to LangChain! Follow these steps to have your pull request considered as ready for review.
+
+1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION
 
-- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
   - Examples:
+    - fix(anthropic): resolve flag parsing error
     - feat(core): add multi-tenant support
-    - fix(cli): resolve flag parsing error
-    - docs(openai): update API usage examples
-  - Allowed `{TYPE}` values:
-    - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
-  - Allowed `{SCOPE}` values (optional):
-    - core, cli, langchain, standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant, xai, infra
-  - Once you've written the title, please delete this checklist item; do not include it in the PR.
+    - test(openai): update API usage tests
+  - Allowed TYPE and SCOPE values: https://github.com/langchain-ai/langchain/blob/master/.github/workflows/pr_lint.yml#L15-L33
 
-- [ ] **PR message**: ***Delete this entire checklist*** and replace with
-  - **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable to a relevant issue.
-  - **Issue:** the issue # it fixes, if applicable (e.g. Fixes #123)
-  - **Dependencies:** any dependencies required for this change
+2. PR description:
 
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. **We will not consider a PR unless these three are passing in CI.** See [contribution guidelines](https://docs.langchain.com/oss/python/contributing) for more.
+  - Write 1-2 sentences summarizing the change.
+  - If this PR addresses a specific issue, please include "Fixes #ISSUE_NUMBER" in the description to automatically close the issue when the PR is merged.
+  - If there are any breaking changes, please clearly describe them.
+  - If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" inthe description.
+
+3. Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified.
+
+  - We will not consider a PR unless these three are passing in CI.
 
 Additional guidelines:
 
-- Most PRs should not touch more than one package.
-- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests. Likewise, please do not update the `uv.lock` files unless you are adding a required dependency.
-- Changes should be backwards compatible.
-- Make sure optional dependencies are imported within a function.
+  - We ask that if you use generative AI for your contribution, you include a disclaimer.
+  - PRs should not touch more than one package unless absolutely necessary.
+  - Do not update the `uv.lock` files unless or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.

.github/workflows/_compile_integration_test.yml

@@ -35,7 +35,7 @@ jobs:
     timeout-minutes: 20
     name: "Python ${{ inputs.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/_lint.yml

@@ -38,7 +38,7 @@ jobs:
     timeout-minutes: 20
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/_release.yml

@@ -54,7 +54,7 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
@@ -105,7 +105,7 @@ jobs:
     outputs:
       release-body: ${{ steps.generate-release-body.outputs.release-body }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain
           path: langchain
@@ -206,7 +206,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: actions/download-artifact@v6
         with:
@@ -237,7 +237,7 @@ jobs:
       contents: read
     timeout-minutes: 20
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
@@ -396,7 +396,7 @@ jobs:
       contents: read
     strategy:
       matrix:
-        partner: [openai, anthropic]
+        partner: [anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -412,7 +412,7 @@ jobs:
       AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
       LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       # We implement this conditional as Github Actions does not have good support
       # for conditionally needing steps. https://github.com/actions/runner/issues/491
@@ -492,7 +492,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
@@ -532,7 +532,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"

.github/workflows/_test.yml

@@ -33,7 +33,7 @@ jobs:
     name: "Python ${{ inputs.python-version }}"
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/_test_pydantic.yml

@@ -36,7 +36,7 @@ jobs:
     name: "Pydantic ~=${{ inputs.pydantic-version }}"
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/auto-label-by-package.yml

@@ -0,0 +1,107 @@
+name: Auto Label Issues by Package
+
+on:
+  issues:
+    types: [opened, edited]
+
+jobs:
+  label-by-package:
+    permissions:
+      issues: write
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Sync package labels
+        uses: actions/github-script@v8
+        with:
+          script: |
+            const body = context.payload.issue.body || "";
+
+            // Extract text under "### Package"
+            const match = body.match(/### Package\s+([\s\S]*?)\n###/i);
+            if (!match) return;
+
+            const packageSection = match[1].trim();
+
+            // Mapping table for package names to labels
+            const mapping = {
+              "langchain": "langchain",
+              "langchain-openai": "openai",
+              "langchain-anthropic": "anthropic",
+              "langchain-classic": "langchain-classic",
+              "langchain-core": "core",
+              "langchain-cli": "cli",
+              "langchain-model-profiles": "model-profiles",
+              "langchain-tests": "standard-tests",
+              "langchain-text-splitters": "text-splitters",
+              "langchain-chroma": "chroma",
+              "langchain-deepseek": "deepseek",
+              "langchain-exa": "exa",
+              "langchain-fireworks": "fireworks",
+              "langchain-groq": "groq",
+              "langchain-huggingface": "huggingface",
+              "langchain-mistralai": "mistralai",
+              "langchain-nomic": "nomic",
+              "langchain-ollama": "ollama",
+              "langchain-perplexity": "perplexity",
+              "langchain-prompty": "prompty",
+              "langchain-qdrant": "qdrant",
+              "langchain-xai": "xai",
+            };
+
+            // All possible package labels we manage
+            const allPackageLabels = Object.values(mapping);
+            const selectedLabels = [];
+
+            // Check if this is checkbox format (multiple selection)
+            const checkboxMatches = packageSection.match(/- \[x\]\s+([^\n\r]+)/gi);
+            if (checkboxMatches) {
+              // Handle checkbox format
+              for (const match of checkboxMatches) {
+                const packageName = match.replace(/- \[x\]\s+/i, '').trim();
+                const label = mapping[packageName];
+                if (label && !selectedLabels.includes(label)) {
+                  selectedLabels.push(label);
+                }
+              }
+            } else {
+              // Handle dropdown format (single selection)
+              const label = mapping[packageSection];
+              if (label) {
+                selectedLabels.push(label);
+              }
+            }
+
+            // Get current issue labels
+            const issue = await github.rest.issues.get({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number
+            });
+
+            const currentLabels = issue.data.labels.map(label => label.name);
+            const currentPackageLabels = currentLabels.filter(label => allPackageLabels.includes(label));
+
+            // Determine labels to add and remove
+            const labelsToAdd = selectedLabels.filter(label => !currentPackageLabels.includes(label));
+            const labelsToRemove = currentPackageLabels.filter(label => !selectedLabels.includes(label));
+
+            // Add new labels
+            if (labelsToAdd.length > 0) {
+              await github.rest.issues.addLabels({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                labels: labelsToAdd
+              });
+            }
+
+            // Remove old labels
+            for (const label of labelsToRemove) {
+              await github.rest.issues.removeLabel({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                name: label
+              });
+            }

.github/workflows/check_core_versions.yml

@@ -18,7 +18,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: "✅ Verify pyproject.toml & version.py Match"
         run: |

.github/workflows/check_diffs.yml

@@ -47,7 +47,7 @@ jobs:
     if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-ignore') }}
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
       - name: "🐍 Setup Python 3.11"
         uses: actions/setup-python@v6
         with:
@@ -141,7 +141,7 @@ jobs:
       run:
         working-directory: ${{ matrix.job-configs.working-directory }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: "🐍 Set up Python ${{ matrix.job-configs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"
@@ -182,7 +182,7 @@ jobs:
         job-configs: ${{ fromJson(needs.build.outputs.codspeed) }}
       fail-fast: false
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: "📦 Install UV Package Manager"
         uses: astral-sh/setup-uv@v7

.github/workflows/integration_tests.yml

@@ -71,14 +71,14 @@ jobs:
         working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           path: langchain
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain-google
           path: langchain-google
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain-aws
           path: langchain-aws

.github/workflows/pr_lint.yml

@@ -26,11 +26,13 @@
 #   * revert     — reverts a previous commit
 #   * release    — prepare a new release
 #
-# Allowed Scopes (optional):
-#   core, cli, langchain, langchain_v1, langchain-classic, standard-tests,
-#   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
-#   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
-#   xai, infra, deps
+# Allowed Scope(s) (optional):
+#   core, cli, langchain, langchain_v1, langchain-classic, model-profiles,
+#   standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa,
+#   fireworks, groq, huggingface, mistralai, nomic, ollama, openai,
+#   perplexity, prompty, qdrant, xai, infra, deps
+#
+# Multiple scopes can be used by separating them with a comma.
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
@@ -100,6 +102,7 @@ jobs:
             qdrant
             xai
             infra
+            deps
           requireScope: false
           disallowScopes: |
             release

.github/workflows/v03_api_doc_build.yml

@@ -23,12 +23,12 @@ jobs:
     permissions:
       contents: read
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: v0.3
           path: langchain
 
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain-api-docs-html
           path: langchain-api-docs-html

.gitignore

@@ -163,3 +163,6 @@ node_modules
 
 prof
 virtualenv/
+scratch/
+
+.langgraph_api/

AGENTS.md

@@ -1,255 +1,58 @@
-# Global Development Guidelines for LangChain Projects
+# Global development guidelines for the LangChain monorepo
 
-## Core Development Principles
+This document provides context to understand the LangChain Python project and assist with development.
 
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
+## Project architecture and context
 
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
+### Monorepo structure
 
-❌ **Bad - Breaking Change:**
+This is a Python monorepo with multiple independently versioned packages that use `uv`.
 
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
+```txt
+langchain/
+├── libs/
+│   ├── core/             # `langchain-core` primitives and base abstractions
+│   ├── langchain/        # `langchain-classic` (legacy, no new features)
+│   ├── langchain_v1/     # Actively maintained `langchain` package
+│   ├── partners/         # Third-party integrations
+│   │   ├── openai/       # OpenAI models and embeddings
+│   │   ├── anthropic/    # Anthropic (Claude) integration
+│   │   ├── ollama/       # Local model support
+│   │   └── ... (other integrations maintained by the LangChain team)
+│   ├── text-splitters/   # Document chunking utilities
+│   ├── standard-tests/   # Shared test suite for integrations
+│   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
+├── .github/              # CI/CD workflows and templates
+├── .vscode/              # VSCode IDE standard settings and recommended extensions
+└── README.md             # Information about LangChain
 ```
 
-✅ **Good - Stable Interface:**
+- **Core layer** (`langchain-core`): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly.
+- **Implementation layer** (`langchain`): Concrete implementations and high-level public utilities
+- **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
+- **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations
 
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
+### Development tools & commands**
 
-**Before making ANY changes to public APIs:**
+- `uv` – Fast Python package installer and resolver (replaces pip/poetry)
+- `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
+- `ruff` – Fast Python linter and formatter
+- `mypy` – Static type checking
+- `pytest` – Testing framework
 
-- Check if the function/class is exported in `__init__.py`
-- Look for existing usage patterns in tests and examples
-- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
-- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`
 
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
-- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
-- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
-- Avoid unnecessary abstraction or premature optimization
-- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
-- Unit tests: `tests/unit_tests/` (no network calls allowed)
-- Integration tests: `tests/integration_tests/` (network calls permitted)
-- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
-- [ ] Tests fail when your new logic is broken
-- [ ] Happy path is covered
-- [ ] Edge cases and error conditions are tested
-- [ ] Use fixtures/mocks for external dependencies
-- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
-- [ ] Does the test suite fail if your new logic is broken?
-- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
-- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
-- No `eval()`, `exec()`, or `pickle` on user-controlled input
-- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
-- Remove unreachable/commented code before committing
-- Race conditions or resource leaks (file handles, sockets, threads).
-- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        `True` if email was sent successfully, `False` otherwise.
-
-    Raises:
-        `InvalidEmailError`: If the email address format is invalid.
-        `SMTPConnectionError`: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
-- Types go in function signatures, NOT in docstrings
-  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
-- Focus on "why" rather than "what" in descriptions
-- Document all parameters, return values, and exceptions
-- Keep descriptions concise but clear
-- Ensure American English spelling (e.g., "behavior", not "behaviour")
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
-- Reduce code duplication through shared utilities
-- Make unit testing easier
-- Improve separation of concerns (single responsibility)
-- Make unit testing easier through dependency injection
-- Add clarity without adding complexity
-- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
+Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
 
 ```bash
 # Run unit tests (no network)
 make test
 
-# Don't run integration tests, as API keys must be set
-
 # Run specific test file
 uv run --group test pytest tests/unit_tests/test_specific.py
 ```
 
-### Code Quality
-
 ```bash
 # Lint code
 make lint
@@ -261,66 +64,118 @@ make format
 uv run --group lint mypy .
 ```
 
-### Dependency Management Patterns
+#### Key config files
 
-**Local Development Dependencies:**
+- pyproject.toml: Main workspace configuration with dependency groups
+- uv.lock: Locked dependencies for reproducible builds
+- Makefile: Development tasks
 
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
+#### Commit standards
 
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
 
-```python
-from langchain_core.tools import tool
+#### Pull request guidelines
 
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
+- Always add a disclaimer to the PR description mentioning how AI agents are involved with the contribution.
+- Describe the "why" of the changes, why the proposed solution is the right one. Limit prose.
+- Highlight areas of the proposed changes that require careful review.
+
+## Core development principles
+
+### Maintain stable public interfaces
+
+CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+
+**Before making ANY changes to public APIs:**
+
+- Check if the function/class is exported in `__init__.py`
+- Look for existing usage patterns in tests and examples
+- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
+- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+
+Ask: "Would this change break someone's code if they used it last week?"
+
+### Code quality standards
+
+All Python code MUST include type hints and return types.
+
+```python title="Example"
+def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
+    """Single line description of the function.
+
+    Any additional context about the function can go here.
 
     Args:
-        query: The search query string.
+        users: List of user identifiers to filter.
+        known_users: Set of known/valid user identifiers.
+
+    Returns:
+        List of users that are not in the known_users set.
     """
-    # Implementation here
-    return results
 ```
 
-## Commit Standards
+- Use descriptive, self-explanatory variable names.
+- Follow existing patterns in the codebase you're modifying
+- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
 
-**Use Conventional Commits format for PR titles:**
+### Testing requirements
 
-- `feat(core): add multi-tenant support`
-- `fix(cli): resolve flag parsing error`
-- `docs: update API usage examples`
-- `docs(openai): update API usage examples`
+Every new feature or bugfix MUST be covered by unit tests.
 
-## Framework-Specific Guidelines
+- Unit tests: `tests/unit_tests/` (no network calls allowed)
+- Integration tests: `tests/integration_tests/` (network calls permitted)
+- We use `pytest` as the testing framework; if in doubt, check other existing tests for examples.
+- The testing file structure should mirror the source code structure.
 
-- Follow the existing patterns in `langchain-core` for base abstractions
-- Use `langchain_core.callbacks` for execution tracking
-- Implement proper streaming support where applicable
-- Avoid deprecated components like legacy `LLMChain`
+**Checklist:**
 
-### Partner Integrations
+- [ ] Tests fail when your new logic is broken
+- [ ] Happy path is covered
+- [ ] Edge cases and error conditions are tested
+- [ ] Use fixtures/mocks for external dependencies
+- [ ] Tests are deterministic (no flaky tests)
+- [ ] Does the test suite fail if your new logic is broken?
 
-- Follow the established patterns in existing partner libraries
-- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
-- Include comprehensive integration tests
-- Document API key requirements and authentication
+### Security and risk assessment
 
----
+- No `eval()`, `exec()`, or `pickle` on user-controlled input
+- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
+- Remove unreachable/commented code before committing
+- Race conditions or resource leaks (file handles, sockets, threads).
+- Ensure proper resource cleanup (file handles, connections)
 
-## Quick Reference Checklist
+### Documentation standards
 
-Before submitting code changes:
+Use Google-style docstrings with Args section for all public functions.
 
-- [ ] **Breaking Changes**: Verified no public API changes
-- [ ] **Type Hints**: All functions have complete type annotations
-- [ ] **Tests**: New functionality is fully tested
-- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
-- [ ] **Documentation**: Google-style docstrings for public functions
-- [ ] **Code Quality**: `make lint` and `make format` pass
-- [ ] **Architecture**: Suggested improvements where applicable
-- [ ] **Commit Message**: Follows Conventional Commits format
+```python title="Example"
+def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
+    """Send an email to a recipient with specified priority.
+
+    Any additional context about the function can go here.
+
+    Args:
+        to: The email address of the recipient.
+        msg: The message body to send.
+        priority: Email priority level.
+
+    Returns:
+        `True` if email was sent successfully, `False` otherwise.
+
+    Raises:
+        InvalidEmailError: If the email address format is invalid.
+        SMTPConnectionError: If unable to connect to email server.
+    """
+```
+
+- Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
+- Focus on "why" rather than "what" in descriptions
+- Document all parameters, return values, and exceptions
+- Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
+
+## Additional resources
+
+- **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)

CLAUDE.md

@@ -1,255 +1,58 @@
-# Global Development Guidelines for LangChain Projects
+# Global development guidelines for the LangChain monorepo
 
-## Core Development Principles
+This document provides context to understand the LangChain Python project and assist with development.
 
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
+## Project architecture and context
 
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
+### Monorepo structure
 
-❌ **Bad - Breaking Change:**
+This is a Python monorepo with multiple independently versioned packages that use `uv`.
 
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
+```txt
+langchain/
+├── libs/
+│   ├── core/             # `langchain-core` primitives and base abstractions
+│   ├── langchain/        # `langchain-classic` (legacy, no new features)
+│   ├── langchain_v1/     # Actively maintained `langchain` package
+│   ├── partners/         # Third-party integrations
+│   │   ├── openai/       # OpenAI models and embeddings
+│   │   ├── anthropic/    # Anthropic (Claude) integration
+│   │   ├── ollama/       # Local model support
+│   │   └── ... (other integrations maintained by the LangChain team)
+│   ├── text-splitters/   # Document chunking utilities
+│   ├── standard-tests/   # Shared test suite for integrations
+│   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
+├── .github/              # CI/CD workflows and templates
+├── .vscode/              # VSCode IDE standard settings and recommended extensions
+└── README.md             # Information about LangChain
 ```
 
-✅ **Good - Stable Interface:**
+- **Core layer** (`langchain-core`): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly.
+- **Implementation layer** (`langchain`): Concrete implementations and high-level public utilities
+- **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
+- **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations
 
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
+### Development tools & commands**
 
-**Before making ANY changes to public APIs:**
+- `uv` – Fast Python package installer and resolver (replaces pip/poetry)
+- `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
+- `ruff` – Fast Python linter and formatter
+- `mypy` – Static type checking
+- `pytest` – Testing framework
 
-- Check if the function/class is exported in `__init__.py`
-- Look for existing usage patterns in tests and examples
-- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
-- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`
 
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
-- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
-- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
-- Avoid unnecessary abstraction or premature optimization
-- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
-- Unit tests: `tests/unit_tests/` (no network calls allowed)
-- Integration tests: `tests/integration_tests/` (network calls permitted)
-- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
-- [ ] Tests fail when your new logic is broken
-- [ ] Happy path is covered
-- [ ] Edge cases and error conditions are tested
-- [ ] Use fixtures/mocks for external dependencies
-- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
-- [ ] Does the test suite fail if your new logic is broken?
-- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
-- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
-- No `eval()`, `exec()`, or `pickle` on user-controlled input
-- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
-- Remove unreachable/commented code before committing
-- Race conditions or resource leaks (file handles, sockets, threads).
-- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        `True` if email was sent successfully, `False` otherwise.
-
-    Raises:
-        `InvalidEmailError`: If the email address format is invalid.
-        `SMTPConnectionError`: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
-- Types go in function signatures, NOT in docstrings
-  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
-- Focus on "why" rather than "what" in descriptions
-- Document all parameters, return values, and exceptions
-- Keep descriptions concise but clear
-- Ensure American English spelling (e.g., "behavior", not "behaviour")
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
-- Reduce code duplication through shared utilities
-- Make unit testing easier
-- Improve separation of concerns (single responsibility)
-- Make unit testing easier through dependency injection
-- Add clarity without adding complexity
-- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
+Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
 
 ```bash
 # Run unit tests (no network)
 make test
 
-# Don't run integration tests, as API keys must be set
-
 # Run specific test file
 uv run --group test pytest tests/unit_tests/test_specific.py
 ```
 
-### Code Quality
-
 ```bash
 # Lint code
 make lint
@@ -261,66 +64,118 @@ make format
 uv run --group lint mypy .
 ```
 
-### Dependency Management Patterns
+#### Key config files
 
-**Local Development Dependencies:**
+- pyproject.toml: Main workspace configuration with dependency groups
+- uv.lock: Locked dependencies for reproducible builds
+- Makefile: Development tasks
 
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
+#### Commit standards
 
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
 
-```python
-from langchain_core.tools import tool
+#### Pull request guidelines
 
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
+- Always add a disclaimer to the PR description mentioning how AI agents are involved with the contribution.
+- Describe the "why" of the changes, why the proposed solution is the right one. Limit prose.
+- Highlight areas of the proposed changes that require careful review.
+
+## Core development principles
+
+### Maintain stable public interfaces
+
+CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+
+**Before making ANY changes to public APIs:**
+
+- Check if the function/class is exported in `__init__.py`
+- Look for existing usage patterns in tests and examples
+- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
+- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+
+Ask: "Would this change break someone's code if they used it last week?"
+
+### Code quality standards
+
+All Python code MUST include type hints and return types.
+
+```python title="Example"
+def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
+    """Single line description of the function.
+
+    Any additional context about the function can go here.
 
     Args:
-        query: The search query string.
+        users: List of user identifiers to filter.
+        known_users: Set of known/valid user identifiers.
+
+    Returns:
+        List of users that are not in the known_users set.
     """
-    # Implementation here
-    return results
 ```
 
-## Commit Standards
+- Use descriptive, self-explanatory variable names.
+- Follow existing patterns in the codebase you're modifying
+- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
 
-**Use Conventional Commits format for PR titles:**
+### Testing requirements
 
-- `feat(core): add multi-tenant support`
-- `fix(cli): resolve flag parsing error`
-- `docs: update API usage examples`
-- `docs(openai): update API usage examples`
+Every new feature or bugfix MUST be covered by unit tests.
 
-## Framework-Specific Guidelines
+- Unit tests: `tests/unit_tests/` (no network calls allowed)
+- Integration tests: `tests/integration_tests/` (network calls permitted)
+- We use `pytest` as the testing framework; if in doubt, check other existing tests for examples.
+- The testing file structure should mirror the source code structure.
 
-- Follow the existing patterns in `langchain-core` for base abstractions
-- Use `langchain_core.callbacks` for execution tracking
-- Implement proper streaming support where applicable
-- Avoid deprecated components like legacy `LLMChain`
+**Checklist:**
 
-### Partner Integrations
+- [ ] Tests fail when your new logic is broken
+- [ ] Happy path is covered
+- [ ] Edge cases and error conditions are tested
+- [ ] Use fixtures/mocks for external dependencies
+- [ ] Tests are deterministic (no flaky tests)
+- [ ] Does the test suite fail if your new logic is broken?
 
-- Follow the established patterns in existing partner libraries
-- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
-- Include comprehensive integration tests
-- Document API key requirements and authentication
+### Security and risk assessment
 
----
+- No `eval()`, `exec()`, or `pickle` on user-controlled input
+- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
+- Remove unreachable/commented code before committing
+- Race conditions or resource leaks (file handles, sockets, threads).
+- Ensure proper resource cleanup (file handles, connections)
 
-## Quick Reference Checklist
+### Documentation standards
 
-Before submitting code changes:
+Use Google-style docstrings with Args section for all public functions.
 
-- [ ] **Breaking Changes**: Verified no public API changes
-- [ ] **Type Hints**: All functions have complete type annotations
-- [ ] **Tests**: New functionality is fully tested
-- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
-- [ ] **Documentation**: Google-style docstrings for public functions
-- [ ] **Code Quality**: `make lint` and `make format` pass
-- [ ] **Architecture**: Suggested improvements where applicable
-- [ ] **Commit Message**: Follows Conventional Commits format
+```python title="Example"
+def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
+    """Send an email to a recipient with specified priority.
+
+    Any additional context about the function can go here.
+
+    Args:
+        to: The email address of the recipient.
+        msg: The message body to send.
+        priority: Email priority level.
+
+    Returns:
+        `True` if email was sent successfully, `False` otherwise.
+
+    Raises:
+        InvalidEmailError: If the email address format is invalid.
+        SMTPConnectionError: If unable to connect to email server.
+    """
+```
+
+- Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
+- Focus on "why" rather than "what" in descriptions
+- Document all parameters, return values, and exceptions
+- Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
+
+## Additional resources
+
+- **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)

MIGRATE.md

@@ -1,9 +0,0 @@
-# Migrating
-
-Please see the following guides for migrating LangChain code:
-
-* Migrate to [LangChain v1.0](https://docs.langchain.com/oss/python/migrate/langchain-v1)
-* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
-* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
-* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
-* Upgrade to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)

libs/cli/langchain_cli/utils/git.py

@@ -6,9 +6,8 @@ import hashlib
 import logging
 import re
 import shutil
-from collections.abc import Sequence
 from pathlib import Path
-from typing import Any, TypedDict
+from typing import TYPE_CHECKING, Any, TypedDict
 
 from git import Repo
 
@@ -18,6 +17,9 @@ from langchain_cli.constants import (
     DEFAULT_GIT_SUBDIRECTORY,
 )
 
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
 logger = logging.getLogger(__name__)
 
 

libs/cli/tests/unit_tests/migrate/cli_runner/case.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
-from .file import File
-from .folder import Folder
+if TYPE_CHECKING:
+    from .file import File
+    from .folder import Folder
 
 
 @dataclass

libs/cli/tests/unit_tests/migrate/cli_runner/folder.py

@@ -1,9 +1,12 @@
 from __future__ import annotations
 
-from pathlib import Path
+from typing import TYPE_CHECKING
 
 from .file import File
 
+if TYPE_CHECKING:
+    from pathlib import Path
+
 
 class Folder:
     def __init__(self, name: str, *files: Folder | File) -> None:

libs/core/README.md

@@ -34,7 +34,7 @@ The LangChain ecosystem is built on top of `langchain-core`. Some of the benefit
 
 ## 📖 Documentation
 
-For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/).
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview).
 
 ## 📕 Releases & Versioning
 

libs/core/langchain_core/callbacks/base.py

@@ -5,13 +5,12 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING, Any
 
-from typing_extensions import Self
-
 if TYPE_CHECKING:
     from collections.abc import Sequence
     from uuid import UUID
 
     from tenacity import RetryCallState
+    from typing_extensions import Self
 
     from langchain_core.agents import AgentAction, AgentFinish
     from langchain_core.documents import Document

libs/core/langchain_core/callbacks/manager.py

@@ -39,7 +39,6 @@ from langchain_core.tracers.context import (
     tracing_v2_callback_var,
 )
 from langchain_core.tracers.langchain import LangChainTracer
-from langchain_core.tracers.schemas import Run
 from langchain_core.tracers.stdout import ConsoleCallbackHandler
 from langchain_core.utils.env import env_var_is_set
 
@@ -52,6 +51,7 @@ if TYPE_CHECKING:
     from langchain_core.documents import Document
     from langchain_core.outputs import ChatGenerationChunk, GenerationChunk, LLMResult
     from langchain_core.runnables.config import RunnableConfig
+    from langchain_core.tracers.schemas import Run
 
 logger = logging.getLogger(__name__)
 

libs/core/langchain_core/indexing/api.py

@@ -6,16 +6,9 @@ import hashlib
 import json
 import uuid
 import warnings
-from collections.abc import (
-    AsyncIterable,
-    AsyncIterator,
-    Callable,
-    Iterable,
-    Iterator,
-    Sequence,
-)
 from itertools import islice
 from typing import (
+    TYPE_CHECKING,
     Any,
     Literal,
     TypedDict,
@@ -29,6 +22,16 @@ from langchain_core.exceptions import LangChainException
 from langchain_core.indexing.base import DocumentIndex, RecordManager
 from langchain_core.vectorstores import VectorStore
 
+if TYPE_CHECKING:
+    from collections.abc import (
+        AsyncIterable,
+        AsyncIterator,
+        Callable,
+        Iterable,
+        Iterator,
+        Sequence,
+    )
+
 # Magic UUID to use as a namespace for hashing.
 # Used to try and generate a unique UUID for each document
 # from hashing the document content and metadata.
@@ -299,6 +302,7 @@ def index(
     are not able to specify the uid of the document.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.25"
+
         Added `scoped_full` cleanup mode.
 
     !!! warning
@@ -637,6 +641,7 @@ async def aindex(
     are not able to specify the uid of the document.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.25"
+
         Added `scoped_full` cleanup mode.
 
     !!! warning

libs/core/langchain_core/language_models/__init__.py

@@ -53,6 +53,10 @@ if TYPE_CHECKING:
         ParrotFakeChatModel,
     )
     from langchain_core.language_models.llms import LLM, BaseLLM
+    from langchain_core.language_models.model_profile import (
+        ModelProfile,
+        ModelProfileRegistry,
+    )
 
 __all__ = (
     "LLM",
@@ -68,6 +72,8 @@ __all__ = (
     "LanguageModelInput",
     "LanguageModelLike",
     "LanguageModelOutput",
+    "ModelProfile",
+    "ModelProfileRegistry",
     "ParrotFakeChatModel",
     "SimpleChatModel",
     "get_tokenizer",
@@ -90,6 +96,8 @@ _dynamic_imports = {
     "GenericFakeChatModel": "fake_chat_models",
     "ParrotFakeChatModel": "fake_chat_models",
     "LLM": "llms",
+    "ModelProfile": "model_profile",
+    "ModelProfileRegistry": "model_profile",
     "BaseLLM": "llms",
     "is_openai_data_block": "_utils",
 }

libs/core/langchain_core/language_models/_utils.py

@@ -140,6 +140,7 @@ def _normalize_messages(
     - LangChain v0 standard content blocks for backward compatibility
 
     !!! warning "Behavior changed in `langchain-core` 1.0.0"
+
         In previous versions, this function returned messages in LangChain v0 format.
         Now, it returns messages in LangChain v1 format, which upgraded chat models now
         expect to receive when passing back in message history. For backward

libs/core/langchain_core/language_models/base.py

@@ -299,6 +299,9 @@ class BaseLanguageModel(
 
         Useful for checking if an input fits in a model's context window.
 
+        This should be overridden by model-specific implementations to provide accurate
+        token counts via model-specific tokenizers.
+
         Args:
             text: The string input to tokenize.
 
@@ -317,9 +320,17 @@ class BaseLanguageModel(
 
         Useful for checking if an input fits in a model's context window.
 
+        This should be overridden by model-specific implementations to provide accurate
+        token counts via model-specific tokenizers.
+
         !!! note
-            The base implementation of `get_num_tokens_from_messages` ignores tool
-            schemas.
+
+            * The base implementation of `get_num_tokens_from_messages` ignores tool
+                schemas.
+            * The base implementation of `get_num_tokens_from_messages` adds additional
+                prefixes to messages in represent user roles, which will add to the
+                overall token count. Model-specific implementations may choose to
+                handle this differently.
 
         Args:
             messages: The message inputs to tokenize.

libs/core/langchain_core/language_models/chat_models.py

@@ -15,7 +15,6 @@ from typing import TYPE_CHECKING, Any, Literal, cast
 from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import override
 
-from langchain_core._api.beta_decorator import beta
 from langchain_core.caches import BaseCache
 from langchain_core.callbacks import (
     AsyncCallbackManager,
@@ -34,6 +33,7 @@ from langchain_core.language_models.base import (
     LangSmithParams,
     LanguageModelInput,
 )
+from langchain_core.language_models.model_profile import ModelProfile
 from langchain_core.load import dumpd, dumps
 from langchain_core.messages import (
     AIMessage,
@@ -76,8 +76,6 @@ from langchain_core.utils.utils import LC_ID_PREFIX, from_env
 if TYPE_CHECKING:
     import uuid
 
-    from langchain_model_profiles import ModelProfile  # type: ignore[import-untyped]
-
     from langchain_core.output_parsers.base import OutputParserLike
     from langchain_core.runnables import Runnable, RunnableConfig
     from langchain_core.tools import BaseTool
@@ -91,7 +89,10 @@ def _generate_response_from_error(error: BaseException) -> list[ChatGeneration]:
             try:
                 metadata["body"] = response.json()
             except Exception:
-                metadata["body"] = getattr(response, "text", None)
+                try:
+                    metadata["body"] = getattr(response, "text", None)
+                except Exception:
+                    metadata["body"] = None
         if hasattr(response, "headers"):
             try:
                 metadata["headers"] = dict(response.headers)
@@ -332,10 +333,25 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     [`langchain-openai`](https://pypi.org/project/langchain-openai)) can also use this
     field to roll out new content formats in a backward-compatible way.
 
-    !!! version-added "Added in `langchain-core` 1.0"
+    !!! version-added "Added in `langchain-core` 1.0.0"
 
     """
 
+    profile: ModelProfile | None = Field(default=None, exclude=True)
+    """Profile detailing model capabilities.
+
+    !!! warning "Beta feature"
+        This is a beta feature. The format of model profiles is subject to change.
+
+    If not specified, automatically loaded from the provider package on initialization
+    if data is available.
+
+    Example profile data includes context window sizes, supported modalities, or support
+    for tool calling, structured output, and other features.
+
+    !!! version-added "Added in `langchain-core` 1.1.0"
+    """
+
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
     )
@@ -1616,7 +1632,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         # }
         ```
 
-        Example: `dict` schema (`include_raw=False`):
+        Example: Dictionary schema (`include_raw=False`):
 
         ```python
         from pydantic import BaseModel
@@ -1644,6 +1660,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         ```
 
         !!! warning "Behavior changed in `langchain-core` 0.2.26"
+
             Added support for `TypedDict` class.
 
         """  # noqa: E501
@@ -1685,40 +1702,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             return RunnableMap(raw=llm) | parser_with_fallback
         return llm | output_parser
 
-    @property
-    @beta()
-    def profile(self) -> ModelProfile:
-        """Return profiling information for the model.
-
-        This property relies on the `langchain-model-profiles` package to retrieve chat
-        model capabilities, such as context window sizes and supported features.
-
-        Raises:
-            ImportError: If `langchain-model-profiles` is not installed.
-
-        Returns:
-            A `ModelProfile` object containing profiling information for the model.
-        """
-        try:
-            from langchain_model_profiles import get_model_profile  # noqa: PLC0415
-        except ImportError as err:
-            informative_error_message = (
-                "To access model profiling information, please install the "
-                "`langchain-model-profiles` package: "
-                "`pip install langchain-model-profiles`."
-            )
-            raise ImportError(informative_error_message) from err
-
-        provider_id = self._llm_type
-        model_name = (
-            # Model name is not standardized across integrations. New integrations
-            # should prefer `model`.
-            getattr(self, "model", None)
-            or getattr(self, "model_name", None)
-            or getattr(self, "model_id", "")
-        )
-        return get_model_profile(provider_id, model_name) or {}
-
 
 class SimpleChatModel(BaseChatModel):
     """Simplified implementation for a chat model to inherit from.

libs/core/langchain_core/language_models/model_profile.py

@@ -0,0 +1,84 @@
+"""Model profile types and utilities."""
+
+from typing_extensions import TypedDict
+
+
+class ModelProfile(TypedDict, total=False):
+    """Model profile.
+
+    !!! warning "Beta feature"
+        This is a beta feature. The format of model profiles is subject to change.
+
+    Provides information about chat model capabilities, such as context window sizes
+    and supported features.
+    """
+
+    # --- Input constraints ---
+
+    max_input_tokens: int
+    """Maximum context window (tokens)"""
+
+    image_inputs: bool
+    """Whether image inputs are supported."""
+    # TODO: add more detail about formats?
+
+    image_url_inputs: bool
+    """Whether [image URL inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    pdf_inputs: bool
+    """Whether [PDF inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    audio_inputs: bool
+    """Whether [audio inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    video_inputs: bool
+    """Whether [video inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    image_tool_message: bool
+    """Whether images can be included in tool messages."""
+
+    pdf_tool_message: bool
+    """Whether PDFs can be included in tool messages."""
+
+    # --- Output constraints ---
+
+    max_output_tokens: int
+    """Maximum output tokens"""
+
+    reasoning_output: bool
+    """Whether the model supports [reasoning / chain-of-thought](https://docs.langchain.com/oss/python/langchain/models#reasoning)"""
+
+    image_outputs: bool
+    """Whether [image outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    audio_outputs: bool
+    """Whether [audio outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    video_outputs: bool
+    """Whether [video outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    # --- Tool calling ---
+    tool_calling: bool
+    """Whether the model supports [tool calling](https://docs.langchain.com/oss/python/langchain/models#tool-calling)"""
+
+    tool_choice: bool
+    """Whether the model supports [tool choice](https://docs.langchain.com/oss/python/langchain/models#forcing-tool-calls)"""
+
+    # --- Structured output ---
+    structured_output: bool
+    """Whether the model supports a native [structured output](https://docs.langchain.com/oss/python/langchain/models#structured-outputs)
+    feature"""
+
+
+ModelProfileRegistry = dict[str, ModelProfile]
+"""Registry mapping model identifiers or names to their ModelProfile."""

libs/core/langchain_core/load/load.py

@@ -61,13 +61,15 @@ class Reviver:
         """Initialize the reviver.
 
         Args:
-            secrets_map: A map of secrets to load. If a secret is not found in
-                the map, it will be loaded from the environment if `secrets_from_env`
-                is True.
+            secrets_map: A map of secrets to load.
+
+                If a secret is not found in the map, it will be loaded from the
+                environment if `secrets_from_env` is `True`.
             valid_namespaces: A list of additional namespaces (modules)
                 to allow to be deserialized.
             secrets_from_env: Whether to load secrets from the environment.
             additional_import_mappings: A dictionary of additional namespace mappings
+
                 You can use this to override default mappings or add new mappings.
             ignore_unserializable_fields: Whether to ignore unserializable fields.
         """
@@ -195,13 +197,15 @@ def loads(
 
     Args:
         text: The string to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True.
+        secrets_map: A map of secrets to load.
+
+            If a secret is not found in the map, it will be loaded from the environment
+            if `secrets_from_env` is `True`.
         valid_namespaces: A list of additional namespaces (modules)
             to allow to be deserialized.
         secrets_from_env: Whether to load secrets from the environment.
         additional_import_mappings: A dictionary of additional namespace mappings
+
             You can use this to override default mappings or add new mappings.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
 
@@ -237,13 +241,15 @@ def load(
 
     Args:
         obj: The object to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True.
+        secrets_map: A map of secrets to load.
+
+            If a secret is not found in the map, it will be loaded from the environment
+            if `secrets_from_env` is `True`.
         valid_namespaces: A list of additional namespaces (modules)
             to allow to be deserialized.
         secrets_from_env: Whether to load secrets from the environment.
         additional_import_mappings: A dictionary of additional namespace mappings
+
             You can use this to override default mappings or add new mappings.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
 
@@ -265,8 +271,6 @@ def load(
             return reviver(loaded_obj)
         if isinstance(obj, list):
             return [_load(o) for o in obj]
-        if isinstance(obj, str) and obj in reviver.secrets_map:
-            return reviver.secrets_map[obj]
         return obj
 
     return _load(obj)

libs/core/langchain_core/messages/ai.py

@@ -124,9 +124,11 @@ class UsageMetadata(TypedDict):
         ```
 
     !!! warning "Behavior changed in `langchain-core` 0.3.9"
+
         Added `input_token_details` and `output_token_details`.
 
     !!! note "LangSmith SDK"
+
         The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
         LangSmith's `UsageMetadata` has additional fields to capture cost information
         used by the LangSmith platform.

libs/core/langchain_core/messages/base.py

@@ -5,11 +5,9 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, cast, overload
 
 from pydantic import ConfigDict, Field
-from typing_extensions import Self
 
 from langchain_core._api.deprecation import warn_deprecated
 from langchain_core.load.serializable import Serializable
-from langchain_core.messages import content as types
 from langchain_core.utils import get_bolded_text
 from langchain_core.utils._merge import merge_dicts, merge_lists
 from langchain_core.utils.interactive_env import is_interactive_env
@@ -17,6 +15,9 @@ from langchain_core.utils.interactive_env import is_interactive_env
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
+    from typing_extensions import Self
+
+    from langchain_core.messages import content as types
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 

libs/core/langchain_core/messages/block_translators/__init__.py

@@ -12,10 +12,11 @@ the implementation in `BaseMessage`.
 
 from __future__ import annotations
 
-from collections.abc import Callable
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from langchain_core.messages import AIMessage, AIMessageChunk
     from langchain_core.messages import content as types
 

libs/core/langchain_core/messages/block_translators/openai.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import json
 import warnings
-from collections.abc import Iterable
 from typing import TYPE_CHECKING, Any, Literal, cast
 
 from langchain_core.language_models._utils import (
@@ -14,6 +13,8 @@ from langchain_core.language_models._utils import (
 from langchain_core.messages import content as types
 
 if TYPE_CHECKING:
+    from collections.abc import Iterable
+
     from langchain_core.messages import AIMessage, AIMessageChunk
 
 

libs/core/langchain_core/messages/content.py

@@ -654,7 +654,7 @@ class PlainTextContentBlock(TypedDict):
 
     !!! note
         Title and context are optional fields that may be passed to the model. See
-        Anthropic [example](https://docs.claude.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content).
+        Anthropic [example](https://platform.claude.com/docs/en/build-with-claude/citations#citable-vs-non-citable-content).
 
     !!! note "Factory function"
         `create_plaintext_block` may also be used as a factory to create a

libs/core/langchain_core/messages/utils.py

@@ -738,8 +738,10 @@ def trim_messages(
             Set to `len` to count the number of **messages** in the chat history.
 
             !!! note
+
                 Use `count_tokens_approximately` to get fast, approximate token
                 counts.
+
                 This is recommended for using `trim_messages` on the hot path, where
                 exact token counting is not necessary.
 

libs/core/langchain_core/output_parsers/pydantic.py

@@ -37,7 +37,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
     def _parser_exception(
         self, e: Exception, json_object: dict
     ) -> OutputParserException:
-        json_string = json.dumps(json_object)
+        json_string = json.dumps(json_object, ensure_ascii=False)
         name = self.pydantic_object.__name__
         msg = f"Failed to parse {name} from completion {json_string}. Got: {e}"
         return OutputParserException(msg, llm_output=json_string)

libs/core/langchain_core/outputs/chat_generation.py

@@ -2,15 +2,17 @@
 
 from __future__ import annotations
 
-from typing import Literal
+from typing import TYPE_CHECKING, Literal
 
 from pydantic import model_validator
-from typing_extensions import Self
 
 from langchain_core.messages import BaseMessage, BaseMessageChunk
 from langchain_core.outputs.generation import Generation
 from langchain_core.utils._merge import merge_dicts
 
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
 
 class ChatGeneration(Generation):
     """A single chat generation output.

libs/core/langchain_core/prompts/base.py

@@ -6,7 +6,7 @@ import contextlib
 import json
 import typing
 from abc import ABC, abstractmethod
-from collections.abc import Callable, Mapping
+from collections.abc import Mapping
 from functools import cached_property
 from pathlib import Path
 from typing import (
@@ -33,6 +33,8 @@ from langchain_core.runnables.config import ensure_config
 from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from langchain_core.documents import Document
 
 

libs/core/langchain_core/prompts/chat.py

@@ -903,23 +903,28 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 5. A string which is shorthand for `("human", template)`; e.g.,
                     `"{user_input}"`
             template_format: Format of the template.
-            input_variables: A list of the names of the variables whose values are
-                required as inputs to the prompt.
-            optional_variables: A list of the names of the variables for placeholder
-                or MessagePlaceholder that are optional.
+            **kwargs: Additional keyword arguments passed to `BasePromptTemplate`,
+                including (but not limited to):
 
-                These variables are auto inferred from the prompt and user need not
-                provide them.
-            partial_variables: A dictionary of the partial variables the prompt
-                template carries.
+                - `input_variables`: A list of the names of the variables whose values
+                    are required as inputs to the prompt.
+                - `optional_variables`: A list of the names of the variables for
+                    placeholder or `MessagePlaceholder` that are optional.
 
-                Partial variables populate the template so that you don't need to pass
-                them in every time you call the prompt.
-            validate_template: Whether to validate the template.
-            input_types: A dictionary of the types of the variables the prompt template
-                expects.
+                    These variables are auto inferred from the prompt and user need not
+                    provide them.
 
-                If not provided, all variables are assumed to be strings.
+                - `partial_variables`: A dictionary of the partial variables the prompt
+                    template carries.
+
+                    Partial variables populate the template so that you don't need to
+                    pass them in every time you call the prompt.
+
+                - `validate_template`: Whether to validate the template.
+                - `input_types`: A dictionary of the types of the variables the prompt
+                    template expects.
+
+                    If not provided, all variables are assumed to be strings.
 
         Examples:
             Instantiation from a list of message templates:

libs/core/langchain_core/prompts/message.py

@@ -6,10 +6,10 @@ from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any
 
 from langchain_core.load import Serializable
-from langchain_core.messages import BaseMessage
 from langchain_core.utils.interactive_env import is_interactive_env
 
 if TYPE_CHECKING:
+    from langchain_core.messages import BaseMessage
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 

libs/core/langchain_core/prompts/string.py

@@ -4,9 +4,8 @@ from __future__ import annotations
 
 import warnings
 from abc import ABC
-from collections.abc import Callable, Sequence
 from string import Formatter
-from typing import Any, Literal
+from typing import TYPE_CHECKING, Any, Literal
 
 from pydantic import BaseModel, create_model
 
@@ -16,10 +15,70 @@ from langchain_core.utils import get_colored_text, mustache
 from langchain_core.utils.formatting import formatter
 from langchain_core.utils.interactive_env import is_interactive_env
 
+if TYPE_CHECKING:
+    from collections.abc import Callable, Sequence
+
 try:
-    from jinja2 import Environment, meta
+    from jinja2 import meta
+    from jinja2.exceptions import SecurityError
     from jinja2.sandbox import SandboxedEnvironment
 
+    class _RestrictedSandboxedEnvironment(SandboxedEnvironment):
+        """A more restrictive Jinja2 sandbox that blocks all attribute/method access.
+
+        This sandbox only allows simple variable lookups, no attribute or method access.
+        This prevents template injection attacks via methods like parse_raw().
+        """
+
+        def is_safe_attribute(self, _obj: Any, _attr: str, _value: Any) -> bool:
+            """Block ALL attribute access for security.
+
+            Only allow accessing variables directly from the context dict,
+            no attribute access on those objects.
+
+            Args:
+                _obj: The object being accessed (unused, always blocked).
+                _attr: The attribute name (unused, always blocked).
+                _value: The attribute value (unused, always blocked).
+
+            Returns:
+                False - all attribute access is blocked.
+            """
+            # Block all attribute access
+            return False
+
+        def is_safe_callable(self, _obj: Any) -> bool:
+            """Block all method calls for security.
+
+            Args:
+                _obj: The object being checked (unused, always blocked).
+
+            Returns:
+                False - all callables are blocked.
+            """
+            return False
+
+        def getattr(self, obj: Any, attribute: str) -> Any:
+            """Override getattr to block all attribute access.
+
+            Args:
+                obj: The object.
+                attribute: The attribute name.
+
+            Returns:
+                Never returns.
+
+            Raises:
+                SecurityError: Always, to block attribute access.
+            """
+            msg = (
+                f"Access to attributes is not allowed in templates. "
+                f"Attempted to access '{attribute}' on {type(obj).__name__}. "
+                f"Use only simple variable names like {{{{variable}}}} "
+                f"without dots or methods."
+            )
+            raise SecurityError(msg)
+
     _HAS_JINJA2 = True
 except ImportError:
     _HAS_JINJA2 = False
@@ -59,14 +118,10 @@ def jinja2_formatter(template: str, /, **kwargs: Any) -> str:
         )
         raise ImportError(msg)
 
-    # This uses a sandboxed environment to prevent arbitrary code execution.
-    # Jinja2 uses an opt-out rather than opt-in approach for sand-boxing.
-    # Please treat this sand-boxing as a best-effort approach rather than
-    # a guarantee of security.
-    # We recommend to never use jinja2 templates with untrusted inputs.
-    # https://jinja.palletsprojects.com/en/3.1.x/sandbox/
-    # approach not a guarantee of security.
-    return SandboxedEnvironment().from_string(template).render(**kwargs)
+    # Use a restricted sandbox that blocks ALL attribute/method access
+    # Only simple variable lookups like {{variable}} are allowed
+    # Attribute access like {{variable.attr}} or {{variable.method()}} is blocked
+    return _RestrictedSandboxedEnvironment().from_string(template).render(**kwargs)
 
 
 def validate_jinja2(template: str, input_variables: list[str]) -> None:
@@ -101,7 +156,7 @@ def _get_jinja2_variables_from_template(template: str) -> set[str]:
             "Please install it with `pip install jinja2`."
         )
         raise ImportError(msg)
-    env = Environment()  # noqa: S701
+    env = _RestrictedSandboxedEnvironment()
     ast = env.parse(template)
     return meta.find_undeclared_variables(ast)
 
@@ -271,6 +326,30 @@ def get_template_variables(template: str, template_format: str) -> list[str]:
         msg = f"Unsupported template format: {template_format}"
         raise ValueError(msg)
 
+    # For f-strings, block attribute access and indexing syntax
+    # This prevents template injection attacks via accessing dangerous attributes
+    if template_format == "f-string":
+        for var in input_variables:
+            # Formatter().parse() returns field names with dots/brackets if present
+            # e.g., "obj.attr" or "obj[0]" - we need to block these
+            if "." in var or "[" in var or "]" in var:
+                msg = (
+                    f"Invalid variable name {var!r} in f-string template. "
+                    f"Variable names cannot contain attribute "
+                    f"access (.) or indexing ([])."
+                )
+                raise ValueError(msg)
+
+            # Block variable names that are all digits (e.g., "0", "100")
+            # These are interpreted as positional arguments, not keyword arguments
+            if var.isdigit():
+                msg = (
+                    f"Invalid variable name {var!r} in f-string template. "
+                    f"Variable names cannot be all digits as they are interpreted "
+                    f"as positional arguments."
+                )
+                raise ValueError(msg)
+
     return sorted(input_variables)
 
 

libs/core/langchain_core/prompts/structured.py

@@ -49,7 +49,13 @@ class StructuredPrompt(ChatPromptTemplate):
             structured_output_kwargs: additional kwargs for structured output.
             template_format: template format for the prompt.
         """
-        schema_ = schema_ or kwargs.pop("schema")
+        schema_ = schema_ or kwargs.pop("schema", None)
+        if not schema_:
+            err_msg = (
+                "Must pass in a non-empty structured output schema. Received: "
+                f"{schema_}"
+            )
+            raise ValueError(err_msg)
         structured_output_kwargs = structured_output_kwargs or {}
         for k in set(kwargs).difference(get_pydantic_field_names(self.__class__)):
             structured_output_kwargs[k] = kwargs.pop(k)

libs/core/langchain_core/runnables/base.py

@@ -707,51 +707,53 @@ class Runnable(ABC, Generic[Input, Output]):
     def pick(self, keys: str | list[str]) -> RunnableSerializable[Any, Any]:
         """Pick keys from the output `dict` of this `Runnable`.
 
-        Pick a single key:
+        !!! example "Pick a single key"
 
-        ```python
-        import json
+            ```python
+            import json
 
-        from langchain_core.runnables import RunnableLambda, RunnableMap
+            from langchain_core.runnables import RunnableLambda, RunnableMap
 
-        as_str = RunnableLambda(str)
-        as_json = RunnableLambda(json.loads)
-        chain = RunnableMap(str=as_str, json=as_json)
+            as_str = RunnableLambda(str)
+            as_json = RunnableLambda(json.loads)
+            chain = RunnableMap(str=as_str, json=as_json)
 
-        chain.invoke("[1, 2, 3]")
-        # -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
+            chain.invoke("[1, 2, 3]")
+            # -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
 
-        json_only_chain = chain.pick("json")
-        json_only_chain.invoke("[1, 2, 3]")
-        # -> [1, 2, 3]
-        ```
+            json_only_chain = chain.pick("json")
+            json_only_chain.invoke("[1, 2, 3]")
+            # -> [1, 2, 3]
+            ```
 
-        Pick a list of keys:
+        !!! example "Pick a list of keys"
 
-        ```python
-        from typing import Any
+            ```python
+            from typing import Any
 
-        import json
+            import json
 
-        from langchain_core.runnables import RunnableLambda, RunnableMap
+            from langchain_core.runnables import RunnableLambda, RunnableMap
 
-        as_str = RunnableLambda(str)
-        as_json = RunnableLambda(json.loads)
+            as_str = RunnableLambda(str)
+            as_json = RunnableLambda(json.loads)
 
 
-        def as_bytes(x: Any) -> bytes:
-            return bytes(x, "utf-8")
+            def as_bytes(x: Any) -> bytes:
+                return bytes(x, "utf-8")
 
 
-        chain = RunnableMap(str=as_str, json=as_json, bytes=RunnableLambda(as_bytes))
+            chain = RunnableMap(
+                str=as_str, json=as_json, bytes=RunnableLambda(as_bytes)
+            )
 
-        chain.invoke("[1, 2, 3]")
-        # -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
+            chain.invoke("[1, 2, 3]")
+            # -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
 
-        json_and_bytes_chain = chain.pick(["json", "bytes"])
-        json_and_bytes_chain.invoke("[1, 2, 3]")
-        # -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
-        ```
+            json_and_bytes_chain = chain.pick(["json", "bytes"])
+            json_and_bytes_chain.invoke("[1, 2, 3]")
+            # -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
+            ```
 
         Args:
             keys: A key or list of keys to pick from the output dict.
@@ -1372,48 +1374,50 @@ class Runnable(ABC, Generic[Input, Output]):
         ).with_config({"run_name": "my_template", "tags": ["my_template"]})
         ```
 
-        For instance:
+        !!! example
 
-        ```python
-        from langchain_core.runnables import RunnableLambda
+            ```python
+            from langchain_core.runnables import RunnableLambda
 
 
-        async def reverse(s: str) -> str:
-            return s[::-1]
+            async def reverse(s: str) -> str:
+                return s[::-1]
 
 
-        chain = RunnableLambda(func=reverse)
+            chain = RunnableLambda(func=reverse)
 
-        events = [event async for event in chain.astream_events("hello", version="v2")]
+            events = [
+                event async for event in chain.astream_events("hello", version="v2")
+            ]
 
-        # Will produce the following events
-        # (run_id, and parent_ids has been omitted for brevity):
-        [
-            {
-                "data": {"input": "hello"},
-                "event": "on_chain_start",
-                "metadata": {},
-                "name": "reverse",
-                "tags": [],
-            },
-            {
-                "data": {"chunk": "olleh"},
-                "event": "on_chain_stream",
-                "metadata": {},
-                "name": "reverse",
-                "tags": [],
-            },
-            {
-                "data": {"output": "olleh"},
-                "event": "on_chain_end",
-                "metadata": {},
-                "name": "reverse",
-                "tags": [],
-            },
-        ]
-        ```
+            # Will produce the following events
+            # (run_id, and parent_ids has been omitted for brevity):
+            [
+                {
+                    "data": {"input": "hello"},
+                    "event": "on_chain_start",
+                    "metadata": {},
+                    "name": "reverse",
+                    "tags": [],
+                },
+                {
+                    "data": {"chunk": "olleh"},
+                    "event": "on_chain_stream",
+                    "metadata": {},
+                    "name": "reverse",
+                    "tags": [],
+                },
+                {
+                    "data": {"output": "olleh"},
+                    "event": "on_chain_end",
+                    "metadata": {},
+                    "name": "reverse",
+                    "tags": [],
+                },
+            ]
+            ```
 
-        ```python title="Example: Dispatch Custom Event"
+        ```python title="Dispatch custom event"
         from langchain_core.callbacks.manager import (
             adispatch_custom_event,
         )
@@ -1447,10 +1451,13 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             input: The input to the `Runnable`.
             config: The config to use for the `Runnable`.
-            version: The version of the schema to use either `'v2'` or `'v1'`.
+            version: The version of the schema to use, either `'v2'` or `'v1'`.
+
                 Users should use `'v2'`.
+
                 `'v1'` is for backwards compatibility and will be deprecated
                 in `0.4.0`.
+
                 No default will be assigned until the API is stabilized.
                 custom events will only be surfaced in `'v2'`.
             include_names: Only include events from `Runnable` objects with matching names.
@@ -1460,6 +1467,7 @@ class Runnable(ABC, Generic[Input, Output]):
             exclude_types: Exclude events from `Runnable` objects with matching types.
             exclude_tags: Exclude events from `Runnable` objects with matching tags.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
+
                 These will be passed to `astream_log` as this implementation
                 of `astream_events` is built on top of `astream_log`.
 
@@ -2476,82 +2484,82 @@ class Runnable(ABC, Generic[Input, Output]):
         Returns:
             A `BaseTool` instance.
 
-        Typed dict input:
+        !!! example "`TypedDict` input"
 
-        ```python
-        from typing_extensions import TypedDict
-        from langchain_core.runnables import RunnableLambda
+            ```python
+            from typing_extensions import TypedDict
+            from langchain_core.runnables import RunnableLambda
 
 
-        class Args(TypedDict):
-            a: int
-            b: list[int]
+            class Args(TypedDict):
+                a: int
+                b: list[int]
 
 
-        def f(x: Args) -> str:
-            return str(x["a"] * max(x["b"]))
+            def f(x: Args) -> str:
+                return str(x["a"] * max(x["b"]))
 
 
-        runnable = RunnableLambda(f)
-        as_tool = runnable.as_tool()
-        as_tool.invoke({"a": 3, "b": [1, 2]})
-        ```
+            runnable = RunnableLambda(f)
+            as_tool = runnable.as_tool()
+            as_tool.invoke({"a": 3, "b": [1, 2]})
+            ```
 
-        `dict` input, specifying schema via `args_schema`:
+        !!! example "`dict` input, specifying schema via `args_schema`"
 
-        ```python
-        from typing import Any
-        from pydantic import BaseModel, Field
-        from langchain_core.runnables import RunnableLambda
+            ```python
+            from typing import Any
+            from pydantic import BaseModel, Field
+            from langchain_core.runnables import RunnableLambda
 
-        def f(x: dict[str, Any]) -> str:
-            return str(x["a"] * max(x["b"]))
+            def f(x: dict[str, Any]) -> str:
+                return str(x["a"] * max(x["b"]))
 
-        class FSchema(BaseModel):
-            \"\"\"Apply a function to an integer and list of integers.\"\"\"
+            class FSchema(BaseModel):
+                \"\"\"Apply a function to an integer and list of integers.\"\"\"
 
-            a: int = Field(..., description="Integer")
-            b: list[int] = Field(..., description="List of ints")
+                a: int = Field(..., description="Integer")
+                b: list[int] = Field(..., description="List of ints")
 
-        runnable = RunnableLambda(f)
-        as_tool = runnable.as_tool(FSchema)
-        as_tool.invoke({"a": 3, "b": [1, 2]})
-        ```
+            runnable = RunnableLambda(f)
+            as_tool = runnable.as_tool(FSchema)
+            as_tool.invoke({"a": 3, "b": [1, 2]})
+            ```
 
-        `dict` input, specifying schema via `arg_types`:
+        !!! example "`dict` input, specifying schema via `arg_types`"
 
-        ```python
-        from typing import Any
-        from langchain_core.runnables import RunnableLambda
+            ```python
+            from typing import Any
+            from langchain_core.runnables import RunnableLambda
 
 
-        def f(x: dict[str, Any]) -> str:
-            return str(x["a"] * max(x["b"]))
+            def f(x: dict[str, Any]) -> str:
+                return str(x["a"] * max(x["b"]))
 
 
-        runnable = RunnableLambda(f)
-        as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
-        as_tool.invoke({"a": 3, "b": [1, 2]})
-        ```
+            runnable = RunnableLambda(f)
+            as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
+            as_tool.invoke({"a": 3, "b": [1, 2]})
+            ```
 
-        `str` input:
+        !!! example "`str` input"
 
-        ```python
-        from langchain_core.runnables import RunnableLambda
+            ```python
+            from langchain_core.runnables import RunnableLambda
 
 
-        def f(x: str) -> str:
-            return x + "a"
+            def f(x: str) -> str:
+                return x + "a"
 
 
-        def g(x: str) -> str:
-            return x + "z"
+            def g(x: str) -> str:
+                return x + "z"
 
 
-        runnable = RunnableLambda(f) | g
-        as_tool = runnable.as_tool()
-        as_tool.invoke("b")
-        ```
+            runnable = RunnableLambda(f) | g
+            as_tool = runnable.as_tool()
+            as_tool.invoke("b")
+            ```
         """
         # Avoid circular import
         from langchain_core.tools import convert_runnable_to_tool  # noqa: PLC0415
@@ -2603,29 +2611,33 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
         Returns:
             A new `Runnable` with the fields configured.
 
-        ```python
-        from langchain_core.runnables import ConfigurableField
-        from langchain_openai import ChatOpenAI
+        !!! example
 
-        model = ChatOpenAI(max_tokens=20).configurable_fields(
-            max_tokens=ConfigurableField(
-                id="output_token_number",
-                name="Max tokens in the output",
-                description="The maximum number of tokens in the output",
+            ```python
+            from langchain_core.runnables import ConfigurableField
+            from langchain_openai import ChatOpenAI
+
+            model = ChatOpenAI(max_tokens=20).configurable_fields(
+                max_tokens=ConfigurableField(
+                    id="output_token_number",
+                    name="Max tokens in the output",
+                    description="The maximum number of tokens in the output",
+                )
             )
-        )
 
-        # max_tokens = 20
-        print("max_tokens_20: ", model.invoke("tell me something about chess").content)
+            # max_tokens = 20
+            print(
+                "max_tokens_20: ", model.invoke("tell me something about chess").content
+            )
 
-        # max_tokens = 200
-        print(
-            "max_tokens_200: ",
-            model.with_config(configurable={"output_token_number": 200})
-            .invoke("tell me something about chess")
-            .content,
-        )
-        ```
+            # max_tokens = 200
+            print(
+                "max_tokens_200: ",
+                model.with_config(configurable={"output_token_number": 200})
+                .invoke("tell me something about chess")
+                .content,
+            )
+            ```
         """
         # Import locally to prevent circular import
         from langchain_core.runnables.configurable import (  # noqa: PLC0415
@@ -2664,29 +2676,31 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
         Returns:
             A new `Runnable` with the alternatives configured.
 
-        ```python
-        from langchain_anthropic import ChatAnthropic
-        from langchain_core.runnables.utils import ConfigurableField
-        from langchain_openai import ChatOpenAI
+        !!! example
 
-        model = ChatAnthropic(
-            model_name="claude-sonnet-4-5-20250929"
-        ).configurable_alternatives(
-            ConfigurableField(id="llm"),
-            default_key="anthropic",
-            openai=ChatOpenAI(),
-        )
+            ```python
+            from langchain_anthropic import ChatAnthropic
+            from langchain_core.runnables.utils import ConfigurableField
+            from langchain_openai import ChatOpenAI
 
-        # uses the default model ChatAnthropic
-        print(model.invoke("which organization created you?").content)
+            model = ChatAnthropic(
+                model_name="claude-sonnet-4-5-20250929"
+            ).configurable_alternatives(
+                ConfigurableField(id="llm"),
+                default_key="anthropic",
+                openai=ChatOpenAI(),
+            )
 
-        # uses ChatOpenAI
-        print(
-            model.with_config(configurable={"llm": "openai"})
-            .invoke("which organization created you?")
-            .content
-        )
-        ```
+            # uses the default model ChatAnthropic
+            print(model.invoke("which organization created you?").content)
+
+            # uses ChatOpenAI
+            print(
+                model.with_config(configurable={"llm": "openai"})
+                .invoke("which organization created you?")
+                .content
+            )
+            ```
         """
         # Import locally to prevent circular import
         from langchain_core.runnables.configurable import (  # noqa: PLC0415

libs/core/langchain_core/runnables/branch.py

@@ -303,7 +303,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
         Args:
             input: The input to the `Runnable`.
-            config: The configuration for the Runna`ble.
+            config: The configuration for the `Runnable`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:

libs/core/langchain_core/runnables/config.py

@@ -47,54 +47,59 @@ class EmptyDict(TypedDict, total=False):
 
 
 class RunnableConfig(TypedDict, total=False):
-    """Configuration for a Runnable."""
+    """Configuration for a `Runnable`.
+
+    See the [reference docs](https://reference.langchain.com/python/langchain_core/runnables/#langchain_core.runnables.RunnableConfig)
+    for more details.
+    """
 
     tags: list[str]
-    """
-    Tags for this call and any sub-calls (eg. a Chain calling an LLM).
+    """Tags for this call and any sub-calls (e.g. a Chain calling an LLM).
+
     You can use these to filter calls.
     """
 
     metadata: dict[str, Any]
-    """
-    Metadata for this call and any sub-calls (eg. a Chain calling an LLM).
+    """Metadata for this call and any sub-calls (e.g. a Chain calling an LLM).
+
     Keys should be strings, values should be JSON-serializable.
     """
 
     callbacks: Callbacks
-    """
-    Callbacks for this call and any sub-calls (eg. a Chain calling an LLM).
+    """Callbacks for this call and any sub-calls (e.g. a Chain calling an LLM).
+
     Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
     """
 
     run_name: str
-    """
-    Name for the tracer run for this call. Defaults to the name of the class.
-    """
+    """Name for the tracer run for this call.
+
+    Defaults to the name of the class."""
 
     max_concurrency: int | None
-    """
-    Maximum number of parallel calls to make. If not provided, defaults to
-    `ThreadPoolExecutor`'s default.
+    """Maximum number of parallel calls to make.
+
+    If not provided, defaults to `ThreadPoolExecutor`'s default.
     """
 
     recursion_limit: int
-    """
-    Maximum number of times a call can recurse. If not provided, defaults to `25`.
+    """Maximum number of times a call can recurse.
+
+    If not provided, defaults to `25`.
     """
 
     configurable: dict[str, Any]
-    """
-    Runtime values for attributes previously made configurable on this `Runnable`,
+    """Runtime values for attributes previously made configurable on this `Runnable`,
     or sub-Runnables, through `configurable_fields` or `configurable_alternatives`.
+
     Check `output_schema` for a description of the attributes that have been made
     configurable.
     """
 
     run_id: uuid.UUID | None
-    """
-    Unique identifier for the tracer run for this call. If not provided, a new UUID
-    will be generated.
+    """Unique identifier for the tracer run for this call.
+
+    If not provided, a new UUID will be generated.
     """
 
 

libs/core/langchain_core/runnables/graph.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import inspect
 from collections import defaultdict
-from collections.abc import Callable
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import (
@@ -22,7 +21,7 @@ from langchain_core.runnables.base import Runnable, RunnableSerializable
 from langchain_core.utils.pydantic import _IgnoreUnserializable, is_basemodel_subclass
 
 if TYPE_CHECKING:
-    from collections.abc import Sequence
+    from collections.abc import Callable, Sequence
 
     from pydantic import BaseModel
 
@@ -642,6 +641,7 @@ class Graph:
         retry_delay: float = 1.0,
         frontmatter_config: dict[str, Any] | None = None,
         base_url: str | None = None,
+        proxies: dict[str, str] | None = None,
     ) -> bytes:
         """Draw the graph as a PNG image using Mermaid.
 
@@ -674,11 +674,10 @@ class Graph:
                 }
                 ```
             base_url: The base URL of the Mermaid server for rendering via API.
-
+            proxies: HTTP/HTTPS proxies for requests (e.g. `{"http": "http://127.0.0.1:7890"}`).
 
         Returns:
             The PNG image as bytes.
-
         """
         # Import locally to prevent circular import
         from langchain_core.runnables.graph_mermaid import (  # noqa: PLC0415
@@ -699,6 +698,7 @@ class Graph:
             padding=padding,
             max_retries=max_retries,
             retry_delay=retry_delay,
+            proxies=proxies,
             base_url=base_url,
         )
 

libs/core/langchain_core/runnables/graph_ascii.py

@@ -7,7 +7,6 @@ from __future__ import annotations
 
 import math
 import os
-from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 try:
@@ -20,6 +19,8 @@ except ImportError:
     _HAS_GRANDALF = False
 
 if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
     from langchain_core.runnables.graph import Edge as LangEdge
 
 

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -281,6 +281,7 @@ def draw_mermaid_png(
     max_retries: int = 1,
     retry_delay: float = 1.0,
     base_url: str | None = None,
+    proxies: dict[str, str] | None = None,
 ) -> bytes:
     """Draws a Mermaid graph as PNG using provided syntax.
 
@@ -293,6 +294,7 @@ def draw_mermaid_png(
         max_retries: Maximum number of retries (MermaidDrawMethod.API).
         retry_delay: Delay between retries (MermaidDrawMethod.API).
         base_url: Base URL for the Mermaid.ink API.
+        proxies: HTTP/HTTPS proxies for requests (e.g. `{"http": "http://127.0.0.1:7890"}`).
 
     Returns:
         PNG image bytes.
@@ -314,6 +316,7 @@ def draw_mermaid_png(
             max_retries=max_retries,
             retry_delay=retry_delay,
             base_url=base_url,
+            proxies=proxies,
         )
     else:
         supported_methods = ", ".join([m.value for m in MermaidDrawMethod])
@@ -405,6 +408,7 @@ def _render_mermaid_using_api(
     file_type: Literal["jpeg", "png", "webp"] | None = "png",
     max_retries: int = 1,
     retry_delay: float = 1.0,
+    proxies: dict[str, str] | None = None,
     base_url: str | None = None,
 ) -> bytes:
     """Renders Mermaid graph using the Mermaid.INK API."""
@@ -445,7 +449,7 @@ def _render_mermaid_using_api(
 
     for attempt in range(max_retries + 1):
         try:
-            response = requests.get(image_url, timeout=10)
+            response = requests.get(image_url, timeout=10, proxies=proxies)
             if response.status_code == requests.codes.ok:
                 img_bytes = response.content
                 if output_file_path is not None:

libs/core/langchain_core/runnables/utils.py

@@ -7,8 +7,7 @@ import asyncio
 import inspect
 import sys
 import textwrap
-from collections.abc import Callable, Mapping, Sequence
-from contextvars import Context
+from collections.abc import Mapping, Sequence
 from functools import lru_cache
 from inspect import signature
 from itertools import groupby
@@ -31,9 +30,11 @@ if TYPE_CHECKING:
         AsyncIterable,
         AsyncIterator,
         Awaitable,
+        Callable,
         Coroutine,
         Iterable,
     )
+    from contextvars import Context
 
     from langchain_core.runnables.schema import StreamEvent
 

libs/core/langchain_core/tools/base.py

@@ -386,6 +386,8 @@ class ToolException(Exception):  # noqa: N818
 
 ArgsSchema = TypeBaseModel | dict[str, Any]
 
+_EMPTY_SET: frozenset[str] = frozenset()
+
 
 class BaseTool(RunnableSerializable[str | dict | ToolCall, Any]):
     """Base class for all LangChain tools.
@@ -569,6 +571,11 @@ class ChildTool(BaseTool):
             self.name, full_schema, fields, fn_description=self.description
         )
 
+    @functools.cached_property
+    def _injected_args_keys(self) -> frozenset[str]:
+        # base implementation doesn't manage injected args
+        return _EMPTY_SET
+
     # --- Runnable ---
 
     @override
@@ -649,6 +656,7 @@ class ChildTool(BaseTool):
             if isinstance(input_args, dict):
                 return tool_input
             if issubclass(input_args, BaseModel):
+                # Check args_schema for InjectedToolCallId
                 for k, v in get_all_basemodel_annotations(input_args).items():
                     if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
                         if tool_call_id is None:
@@ -664,6 +672,7 @@ class ChildTool(BaseTool):
                 result = input_args.model_validate(tool_input)
                 result_dict = result.model_dump()
             elif issubclass(input_args, BaseModelV1):
+                # Check args_schema for InjectedToolCallId
                 for k, v in get_all_basemodel_annotations(input_args).items():
                     if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
                         if tool_call_id is None:
@@ -683,9 +692,25 @@ class ChildTool(BaseTool):
                     f"args_schema must be a Pydantic BaseModel, got {self.args_schema}"
                 )
                 raise NotImplementedError(msg)
-            return {
-                k: getattr(result, k) for k, v in result_dict.items() if k in tool_input
+            validated_input = {
+                k: getattr(result, k) for k in result_dict if k in tool_input
             }
+            for k in self._injected_args_keys:
+                if k == "tool_call_id":
+                    if tool_call_id is None:
+                        msg = (
+                            "When tool includes an InjectedToolCallId "
+                            "argument, tool must always be invoked with a full "
+                            "model ToolCall of the form: {'args': {...}, "
+                            "'name': '...', 'type': 'tool_call', "
+                            "'tool_call_id': '...'}"
+                        )
+                        raise ValueError(msg)
+                    validated_input[k] = tool_call_id
+                if k in tool_input:
+                    injected_val = tool_input[k]
+                    validated_input[k] = injected_val
+            return validated_input
         return tool_input
 
     @abstractmethod

libs/core/langchain_core/tools/structured.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import functools
 import textwrap
 from collections.abc import Awaitable, Callable
 from inspect import signature
@@ -21,10 +22,12 @@ from langchain_core.callbacks import (
 )
 from langchain_core.runnables import RunnableConfig, run_in_executor
 from langchain_core.tools.base import (
+    _EMPTY_SET,
     FILTERED_ARGS,
     ArgsSchema,
     BaseTool,
     _get_runnable_config_param,
+    _is_injected_arg_type,
     create_schema_from_function,
 )
 from langchain_core.utils.pydantic import is_basemodel_subclass
@@ -241,6 +244,17 @@ class StructuredTool(BaseTool):
             **kwargs,
         )
 
+    @functools.cached_property
+    def _injected_args_keys(self) -> frozenset[str]:
+        fn = self.func or self.coroutine
+        if fn is None:
+            return _EMPTY_SET
+        return frozenset(
+            k
+            for k, v in signature(fn).parameters.items()
+            if _is_injected_arg_type(v.annotation)
+        )
+
 
 def _filter_schema_args(func: Callable) -> list[str]:
     filter_args = list(FILTERED_ARGS)

libs/core/langchain_core/tracers/core.py

@@ -15,12 +15,6 @@ from typing import (
 
 from langchain_core.exceptions import TracerException
 from langchain_core.load import dumpd
-from langchain_core.outputs import (
-    ChatGeneration,
-    ChatGenerationChunk,
-    GenerationChunk,
-    LLMResult,
-)
 from langchain_core.tracers.schemas import Run
 
 if TYPE_CHECKING:
@@ -31,6 +25,12 @@ if TYPE_CHECKING:
 
     from langchain_core.documents import Document
     from langchain_core.messages import BaseMessage
+    from langchain_core.outputs import (
+        ChatGeneration,
+        ChatGenerationChunk,
+        GenerationChunk,
+        LLMResult,
+    )
 
 logger = logging.getLogger(__name__)
 

libs/core/langchain_core/utils/function_calling.py

@@ -8,7 +8,6 @@ import logging
 import types
 import typing
 import uuid
-from collections.abc import Callable
 from typing import (
     TYPE_CHECKING,
     Annotated,
@@ -33,6 +32,8 @@ from langchain_core.utils.json_schema import dereference_refs
 from langchain_core.utils.pydantic import is_basemodel_subclass
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from langchain_core.tools import BaseTool
 
 logger = logging.getLogger(__name__)
@@ -352,6 +353,7 @@ def convert_to_openai_function(
         ValueError: If function is not in a supported format.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.16"
+
         `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
     """
@@ -476,15 +478,18 @@ def convert_to_openai_tool(
         OpenAI tool-calling API.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.16"
+
         `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.44"
+
         Return OpenAI Responses API-style tools unchanged. This includes
         any dict with `"type"` in `"file_search"`, `"function"`,
         `"computer_use_preview"`, `"web_search_preview"`.
 
     !!! warning "Behavior changed in `langchain-core` 0.3.63"
+
         Added support for OpenAI's image generation built-in tool.
     """
     # Import locally to prevent circular import

libs/core/langchain_core/utils/json.py

@@ -4,11 +4,13 @@ from __future__ import annotations
 
 import json
 import re
-from collections.abc import Callable
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from langchain_core.exceptions import OutputParserException
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
 
 def _replace_new_line(match: re.Match[str]) -> str:
     value = match.group(2)

libs/core/langchain_core/utils/json_schema.py

@@ -170,28 +170,33 @@ def dereference_refs(
     full_schema: dict | None = None,
     skip_keys: Sequence[str] | None = None,
 ) -> dict:
-    """Resolve and inline JSON Schema $ref references in a schema object.
+    """Resolve and inline JSON Schema `$ref` references in a schema object.
 
-    This function processes a JSON Schema and resolves all $ref references by replacing
-    them with the actual referenced content. It handles both simple references and
-    complex cases like circular references and mixed $ref objects that contain
-    additional properties alongside the $ref.
+    This function processes a JSON Schema and resolves all `$ref` references by
+    replacing them with the actual referenced content.
+
+    Handles both simple references and complex cases like circular references and mixed
+    `$ref` objects that contain additional properties alongside the `$ref`.
 
     Args:
-        schema_obj: The JSON Schema object or fragment to process. This can be a
-            complete schema or just a portion of one.
-        full_schema: The complete schema containing all definitions that $refs might
-            point to. If not provided, defaults to schema_obj (useful when the
-            schema is self-contained).
-        skip_keys: Controls recursion behavior and reference resolution depth:
-            - If `None` (Default): Only recurse under '$defs' and use shallow reference
-              resolution (break cycles but don't deep-inline nested refs)
-            - If provided (even as []): Recurse under all keys and use deep reference
-              resolution (fully inline all nested references)
+        schema_obj: The JSON Schema object or fragment to process.
+
+            This can be a complete schema or just a portion of one.
+        full_schema: The complete schema containing all definitions that `$refs` might
+            point to.
+
+            If not provided, defaults to `schema_obj` (useful when the schema is
+            self-contained).
+        skip_keys: Controls recursion behavior and reference resolution depth.
+
+            - If `None` (Default): Only recurse under `'$defs'` and use shallow
+                reference resolution (break cycles but don't deep-inline nested refs)
+            - If provided (even as `[]`): Recurse under all keys and use deep reference
+                resolution (fully inline all nested references)
 
     Returns:
-        A new dictionary with all $ref references resolved and inlined. The original
-        schema_obj is not modified.
+        A new dictionary with all $ref references resolved and inlined.
+            The original `schema_obj` is not modified.
 
     Examples:
         Basic reference resolution:
@@ -203,7 +208,8 @@ def dereference_refs(
         >>> result = dereference_refs(schema)
         >>> result["properties"]["name"]  # {"type": "string"}
 
-        Mixed $ref with additional properties:
+        Mixed `$ref` with additional properties:
+
         >>> schema = {
         ...     "properties": {
         ...         "name": {"$ref": "#/$defs/base", "description": "User name"}
@@ -215,6 +221,7 @@ def dereference_refs(
         # {"type": "string", "minLength": 1, "description": "User name"}
 
         Handling circular references:
+
         >>> schema = {
         ...     "properties": {"user": {"$ref": "#/$defs/User"}},
         ...     "$defs": {
@@ -227,10 +234,11 @@ def dereference_refs(
         >>> result = dereference_refs(schema)  # Won't cause infinite recursion
 
     !!! note
+
         - Circular references are handled gracefully by breaking cycles
-        - Mixed $ref objects (with both $ref and other properties) are supported
-        - Additional properties in mixed $refs override resolved properties
-        - The $defs section is preserved in the output by default
+        - Mixed `$ref` objects (with both `$ref` and other properties) are supported
+        - Additional properties in mixed `$refs` override resolved properties
+        - The `$defs` section is preserved in the output by default
     """
     full = full_schema or schema_obj
     keys_to_skip = list(skip_keys) if skip_keys is not None else ["$defs"]

libs/core/langchain_core/utils/mustache.py

@@ -374,15 +374,29 @@ def _get_key(
                 if resolved_scope in (0, False):
                     return resolved_scope
                 # Move into the scope
-                try:
-                    # Try subscripting (Normal dictionaries)
-                    resolved_scope = cast("dict[str, Any]", resolved_scope)[child]
-                except (TypeError, AttributeError):
+                if isinstance(resolved_scope, dict):
                     try:
-                        resolved_scope = getattr(resolved_scope, child)
-                    except (TypeError, AttributeError):
-                        # Try as a list
-                        resolved_scope = resolved_scope[int(child)]  # type: ignore[index]
+                        resolved_scope = resolved_scope[child]
+                    except (KeyError, TypeError):
+                        # Key not found - will be caught by outer try-except
+                        msg = f"Key {child!r} not found in dict"
+                        raise KeyError(msg) from None
+                elif isinstance(resolved_scope, (list, tuple)):
+                    try:
+                        resolved_scope = resolved_scope[int(child)]
+                    except (ValueError, IndexError, TypeError):
+                        # Invalid index - will be caught by outer try-except
+                        msg = f"Invalid index {child!r} for list/tuple"
+                        raise IndexError(msg) from None
+                else:
+                    # Reject everything else for security
+                    # This prevents traversing into arbitrary Python objects
+                    msg = (
+                        f"Cannot traverse into {type(resolved_scope).__name__}. "
+                        "Mustache templates only support dict, list, and tuple. "
+                        f"Got: {type(resolved_scope)}"
+                    )
+                    raise TypeError(msg)  # noqa: TRY301
 
             try:
                 # This allows for custom falsy data types
@@ -393,8 +407,9 @@ def _get_key(
                 if resolved_scope in (0, False):
                     return resolved_scope
                 return resolved_scope or ""
-        except (AttributeError, KeyError, IndexError, ValueError):
+        except (AttributeError, KeyError, IndexError, ValueError, TypeError):
             # We couldn't find the key in the current scope
+            # TypeError: Attempted to traverse into non-dict/list type
             # We'll try again on the next pass
             pass
 

libs/core/langchain_core/utils/pydantic.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 import inspect
 import textwrap
 import warnings
-from collections.abc import Callable
 from contextlib import nullcontext
 from functools import lru_cache, wraps
 from types import GenericAlias
@@ -41,10 +40,12 @@ from pydantic.json_schema import (
 )
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import create_model as create_model_v1
-from pydantic.v1.fields import ModelField
 from typing_extensions import deprecated, override
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from pydantic.v1.fields import ModelField
     from pydantic_core import core_schema
 
 PYDANTIC_VERSION = version.parse(pydantic.__version__)

libs/core/langchain_core/vectorstores/base.py

@@ -11,7 +11,6 @@ import logging
 import math
 import warnings
 from abc import ABC, abstractmethod
-from collections.abc import Callable
 from itertools import cycle
 from typing import (
     TYPE_CHECKING,
@@ -29,7 +28,7 @@ from langchain_core.retrievers import BaseRetriever, LangSmithRetrieverParams
 from langchain_core.runnables.config import run_in_executor
 
 if TYPE_CHECKING:
-    from collections.abc import Collection, Iterable, Iterator, Sequence
+    from collections.abc import Callable, Collection, Iterable, Iterator, Sequence
 
     from langchain_core.callbacks.manager import (
         AsyncCallbackManagerForRetrieverRun,
@@ -295,8 +294,9 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            search_type: Type of search to perform. Can be `'similarity'`, `'mmr'`, or
-                `'similarity_score_threshold'`.
+            search_type: Type of search to perform.
+
+                Can be `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -329,8 +329,9 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            search_type: Type of search to perform. Can be `'similarity'`, `'mmr'`, or
-                `'similarity_score_threshold'`.
+            search_type: Type of search to perform.
+
+                Can be `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -461,9 +462,10 @@ class VectorStore(ABC):
         Args:
             query: Input text.
             k: Number of `Document` objects to return.
-            **kwargs: kwargs to be passed to similarity search. Should include
-                `score_threshold`, An optional floating point value between `0` to `1`
-                to filter the resulting set of retrieved docs
+            **kwargs: Kwargs to be passed to similarity search.
+
+                Should include `score_threshold`, an optional floating point value
+                between `0` to `1` to filter the resulting set of retrieved docs.
 
         Returns:
             List of tuples of `(doc, similarity_score)`
@@ -488,9 +490,10 @@ class VectorStore(ABC):
         Args:
             query: Input text.
             k: Number of `Document` objects to return.
-            **kwargs: kwargs to be passed to similarity search. Should include
-                `score_threshold`, An optional floating point value between `0` to `1`
-                to filter the resulting set of retrieved docs
+            **kwargs: Kwargs to be passed to similarity search.
+
+                Should include `score_threshold`, an optional floating point value
+                between `0` to `1` to filter the resulting set of retrieved docs.
 
         Returns:
             List of tuples of `(doc, similarity_score)`
@@ -512,9 +515,10 @@ class VectorStore(ABC):
         Args:
             query: Input text.
             k: Number of `Document` objects to return.
-            **kwargs: kwargs to be passed to similarity search. Should include
-                `score_threshold`, An optional floating point value between `0` to `1`
-                to filter the resulting set of retrieved docs
+            **kwargs: Kwargs to be passed to similarity search.
+
+                Should include `score_threshold`, an optional floating point value
+                between `0` to `1` to filter the resulting set of retrieved docs.
 
         Returns:
             List of tuples of `(doc, similarity_score)`.
@@ -561,9 +565,10 @@ class VectorStore(ABC):
         Args:
             query: Input text.
             k: Number of `Document` objects to return.
-            **kwargs: kwargs to be passed to similarity search. Should include
-                `score_threshold`, An optional floating point value between `0` to `1`
-                to filter the resulting set of retrieved docs
+            **kwargs: Kwargs to be passed to similarity search.
+
+                Should include `score_threshold`, an optional floating point value
+                between `0` to `1` to filter the resulting set of retrieved docs.
 
         Returns:
             List of tuples of `(doc, similarity_score)`
@@ -901,13 +906,15 @@ class VectorStore(ABC):
 
         Args:
             **kwargs: Keyword arguments to pass to the search function.
+
                 Can include:
 
                 * `search_type`: Defines the type of search that the Retriever should
                     perform. Can be `'similarity'` (default), `'mmr'`, or
                     `'similarity_score_threshold'`.
-                * `search_kwargs`: Keyword arguments to pass to the search function. Can
-                    include things like:
+                * `search_kwargs`: Keyword arguments to pass to the search function.
+
+                    Can include things like:
 
                     * `k`: Amount of documents to return (Default: `4`)
                     * `score_threshold`: Minimum relevance threshold

libs/core/langchain_core/vectorstores/in_memory.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import json
 import uuid
-from collections.abc import Callable
 from pathlib import Path
 from typing import (
     TYPE_CHECKING,
@@ -20,7 +19,7 @@ from langchain_core.vectorstores.utils import _cosine_similarity as cosine_simil
 from langchain_core.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:
-    from collections.abc import Iterator, Sequence
+    from collections.abc import Callable, Iterator, Sequence
 
     from langchain_core.embeddings import Embeddings
 

libs/core/langchain_core/version.py

@@ -1,3 +1,3 @@
 """langchain-core version information and utilities."""
 
-VERSION = "1.0.4"
+VERSION = "1.1.0"

libs/core/pyproject.toml

@@ -9,7 +9,7 @@ license = {text = "MIT"}
 readme = "README.md"
 authors = []
 
-version = "1.0.4"
+version = "1.1.0"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "langsmith>=0.3.45,<1.0.0",
@@ -36,7 +36,6 @@ typing = [
     "mypy>=1.18.1,<1.19.0",
     "types-pyyaml>=6.0.12.2,<7.0.0.0",
     "types-requests>=2.28.11.5,<3.0.0.0",
-    "langchain-model-profiles",
     "langchain-text-splitters",
 ]
 dev = [
@@ -58,7 +57,6 @@ test = [
     "blockbuster>=1.5.18,<1.6.0",
     "numpy>=1.26.4; python_version<'3.13'",
     "numpy>=2.1.0; python_version>='3.13'",
-    "langchain-model-profiles",
     "langchain-tests",
     "pytest-benchmark",
     "pytest-codspeed",
@@ -66,7 +64,6 @@ test = [
 test_integration = []
 
 [tool.uv.sources]
-langchain-model-profiles = { path = "../model-profiles" }
 langchain-tests = { path = "../standard-tests" }
 langchain-text-splitters = { path = "../text-splitters" }
 

libs/core/tests/unit_tests/language_models/chat_models/test_base.py

@@ -18,6 +18,7 @@ from langchain_core.language_models import (
     ParrotFakeChatModel,
 )
 from langchain_core.language_models._utils import _normalize_messages
+from langchain_core.language_models.chat_models import _generate_response_from_error
 from langchain_core.language_models.fake_chat_models import (
     FakeListChatModelError,
     GenericFakeChatModel,
@@ -1221,16 +1222,99 @@ def test_get_ls_params() -> None:
 
 def test_model_profiles() -> None:
     model = GenericFakeChatModel(messages=iter([]))
-    profile = model.profile
-    assert profile == {}
+    assert model.profile is None
 
-    class MyModel(GenericFakeChatModel):
-        model: str = "gpt-5"
+    model_with_profile = GenericFakeChatModel(
+        messages=iter([]), profile={"max_input_tokens": 100}
+    )
+    assert model_with_profile.profile == {"max_input_tokens": 100}
 
-        @property
-        def _llm_type(self) -> str:
-            return "openai-chat"
 
-    model = MyModel(messages=iter([]))
-    profile = model.profile
-    assert profile
+class MockResponse:
+    """Mock response for testing _generate_response_from_error."""
+
+    def __init__(
+        self,
+        status_code: int = 400,
+        headers: dict[str, str] | None = None,
+        json_data: dict[str, Any] | None = None,
+        json_raises: type[Exception] | None = None,
+        text_raises: type[Exception] | None = None,
+    ):
+        self.status_code = status_code
+        self.headers = headers or {}
+        self._json_data = json_data
+        self._json_raises = json_raises
+        self._text_raises = text_raises
+
+    def json(self) -> dict[str, Any]:
+        if self._json_raises:
+            msg = "JSON parsing failed"
+            raise self._json_raises(msg)
+        return self._json_data or {}
+
+    @property
+    def text(self) -> str:
+        if self._text_raises:
+            msg = "Text access failed"
+            raise self._text_raises(msg)
+        return ""
+
+
+class MockAPIError(Exception):
+    """Mock API error with response attribute."""
+
+    def __init__(self, message: str, response: MockResponse | None = None):
+        super().__init__(message)
+        self.message = message
+        if response is not None:
+            self.response = response
+
+
+def test_generate_response_from_error_with_valid_json() -> None:
+    """Test `_generate_response_from_error` with valid JSON response."""
+    response = MockResponse(
+        status_code=400,
+        headers={"content-type": "application/json"},
+        json_data={"error": {"message": "Bad request", "type": "invalid_request"}},
+    )
+    error = MockAPIError("API Error", response=response)
+
+    generations = _generate_response_from_error(error)
+
+    assert len(generations) == 1
+    generation = generations[0]
+    assert isinstance(generation, ChatGeneration)
+    assert isinstance(generation.message, AIMessage)
+    assert generation.message.content == ""
+
+    metadata = generation.message.response_metadata
+    assert metadata["body"] == {
+        "error": {"message": "Bad request", "type": "invalid_request"}
+    }
+    assert metadata["headers"] == {"content-type": "application/json"}
+    assert metadata["status_code"] == 400
+
+
+def test_generate_response_from_error_handles_streaming_response_failure() -> None:
+    # Simulates scenario where accessing response.json() or response.text
+    # raises ResponseNotRead on streaming responses
+    response = MockResponse(
+        status_code=400,
+        headers={"content-type": "application/json"},
+        json_raises=Exception,  # Simulates ResponseNotRead or similar
+        text_raises=Exception,
+    )
+    error = MockAPIError("API Error", response=response)
+
+    # This should NOT raise an exception, but should handle it gracefully
+    generations = _generate_response_from_error(error)
+
+    assert len(generations) == 1
+    generation = generations[0]
+    metadata = generation.message.response_metadata
+
+    # When both fail, body should be None instead of raising an exception
+    assert metadata["body"] is None
+    assert metadata["headers"] == {"content-type": "application/json"}
+    assert metadata["status_code"] == 400

libs/core/tests/unit_tests/language_models/test_imports.py

@@ -18,6 +18,8 @@ EXPECTED_ALL = [
     "FakeStreamingListLLM",
     "FakeListLLM",
     "ParrotFakeChatModel",
+    "ModelProfile",
+    "ModelProfileRegistry",
     "is_openai_data_block",
 ]
 

libs/core/tests/unit_tests/load/test_load.py

@@ -1,11 +0,0 @@
-"""Test for Serializable base class."""
-
-from langchain_core.load.load import load
-
-
-def test_load_with_string_secrets() -> None:
-    obj = {"api_key": "__SECRET_API_KEY__"}
-    secrets_map = {"__SECRET_API_KEY__": "hello"}
-    result = load(obj, secrets_map=secrets_map)
-
-    assert result["api_key"] == "hello"

libs/core/tests/unit_tests/messages/block_translators/test_groq.py

@@ -0,0 +1,140 @@
+"""Test groq block translator."""
+
+from typing import cast
+
+import pytest
+
+from langchain_core.messages import AIMessage
+from langchain_core.messages import content as types
+from langchain_core.messages.base import _extract_reasoning_from_additional_kwargs
+from langchain_core.messages.block_translators import PROVIDER_TRANSLATORS
+from langchain_core.messages.block_translators.groq import (
+    _parse_code_json,
+    translate_content,
+)
+
+
+def test_groq_translator_registered() -> None:
+    """Test that groq translator is properly registered."""
+    assert "groq" in PROVIDER_TRANSLATORS
+    assert "translate_content" in PROVIDER_TRANSLATORS["groq"]
+    assert "translate_content_chunk" in PROVIDER_TRANSLATORS["groq"]
+
+
+def test_extract_reasoning_from_additional_kwargs_exists() -> None:
+    """Test that _extract_reasoning_from_additional_kwargs can be imported."""
+    # Verify it's callable
+    assert callable(_extract_reasoning_from_additional_kwargs)
+
+
+def test_groq_translate_content_basic() -> None:
+    """Test basic groq content translation."""
+    # Test with simple text message
+    message = AIMessage(content="Hello world")
+    blocks = translate_content(message)
+
+    assert isinstance(blocks, list)
+    assert len(blocks) == 1
+    assert blocks[0]["type"] == "text"
+    assert blocks[0]["text"] == "Hello world"
+
+
+def test_groq_translate_content_with_reasoning() -> None:
+    """Test groq content translation with reasoning content."""
+    # Test with reasoning content in additional_kwargs
+    message = AIMessage(
+        content="Final answer",
+        additional_kwargs={"reasoning_content": "Let me think about this..."},
+    )
+    blocks = translate_content(message)
+
+    assert isinstance(blocks, list)
+    assert len(blocks) == 2
+
+    # First block should be reasoning
+    assert blocks[0]["type"] == "reasoning"
+    assert blocks[0]["reasoning"] == "Let me think about this..."
+
+    # Second block should be text
+    assert blocks[1]["type"] == "text"
+    assert blocks[1]["text"] == "Final answer"
+
+
+def test_groq_translate_content_with_tool_calls() -> None:
+    """Test groq content translation with tool calls."""
+    # Test with tool calls
+    message = AIMessage(
+        content="",
+        tool_calls=[
+            {
+                "name": "search",
+                "args": {"query": "test"},
+                "id": "call_123",
+            }
+        ],
+    )
+    blocks = translate_content(message)
+
+    assert isinstance(blocks, list)
+    assert len(blocks) == 1
+    assert blocks[0]["type"] == "tool_call"
+    assert blocks[0]["name"] == "search"
+    assert blocks[0]["args"] == {"query": "test"}
+    assert blocks[0]["id"] == "call_123"
+
+
+def test_groq_translate_content_with_executed_tools() -> None:
+    """Test groq content translation with executed tools (built-in tools)."""
+    # Test with executed_tools in additional_kwargs (Groq built-in tools)
+    message = AIMessage(
+        content="",
+        additional_kwargs={
+            "executed_tools": [
+                {
+                    "type": "python",
+                    "arguments": '{"code": "print(\\"hello\\")"}',
+                    "output": "hello\\n",
+                }
+            ]
+        },
+    )
+    blocks = translate_content(message)
+
+    assert isinstance(blocks, list)
+    # Should have server_tool_call and server_tool_result
+    assert len(blocks) >= 2
+
+    # Check for server_tool_call
+    tool_call_blocks = [
+        cast("types.ServerToolCall", b)
+        for b in blocks
+        if b.get("type") == "server_tool_call"
+    ]
+    assert len(tool_call_blocks) == 1
+    assert tool_call_blocks[0]["name"] == "code_interpreter"
+    assert "code" in tool_call_blocks[0]["args"]
+
+    # Check for server_tool_result
+    tool_result_blocks = [
+        cast("types.ServerToolResult", b)
+        for b in blocks
+        if b.get("type") == "server_tool_result"
+    ]
+    assert len(tool_result_blocks) == 1
+    assert tool_result_blocks[0]["output"] == "hello\\n"
+    assert tool_result_blocks[0]["status"] == "success"
+
+
+def test_parse_code_json() -> None:
+    """Test the _parse_code_json helper function."""
+    # Test valid code JSON
+    result = _parse_code_json('{"code": "print(\'hello\')"}')
+    assert result == {"code": "print('hello')"}
+
+    # Test code with unescaped quotes (Groq format)
+    result = _parse_code_json('{"code": "print("hello")"}')
+    assert result == {"code": 'print("hello")'}
+
+    # Test invalid format raises ValueError
+    with pytest.raises(ValueError, match="Could not extract Python code"):
+        _parse_code_json('{"invalid": "format"}')

libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr

@@ -1320,9 +1320,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -2734,9 +2736,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.

libs/core/tests/unit_tests/prompts/test_chat.py

@@ -1540,3 +1540,164 @@ def test_rendering_prompt_with_conditionals_no_empty_text_blocks() -> None:
     assert not [
         block for block in content if block["type"] == "text" and block["text"] == ""
     ]
+
+
+def test_fstring_rejects_invalid_identifier_variable_names() -> None:
+    """Test that f-string templates block attribute access, indexing.
+
+    This validation prevents template injection attacks by blocking:
+    - Attribute access like {msg.__class__}
+    - Indexing like {msg[0]}
+    - All-digit variable names like {0} or {100} (interpreted as positional args)
+
+    While allowing any other field names that Python's Formatter accepts.
+    """
+    # Test that attribute access and indexing are blocked (security issue)
+    invalid_templates = [
+        "{msg.__class__}",  # Attribute access with dunder
+        "{msg.__class__.__name__}",  # Multiple dunders
+        "{msg.content}",  # Attribute access
+        "{msg[0]}",  # Item access
+        "{0}",  # All-digit variable name (positional argument)
+        "{100}",  # All-digit variable name (positional argument)
+        "{42}",  # All-digit variable name (positional argument)
+    ]
+
+    for template_str in invalid_templates:
+        with pytest.raises(ValueError, match="Invalid variable name") as exc_info:
+            ChatPromptTemplate.from_messages(
+                [("human", template_str)],
+                template_format="f-string",
+            )
+
+        error_msg = str(exc_info.value)
+        assert "Invalid variable name" in error_msg
+        # Check for any of the expected error message parts
+        assert (
+            "attribute access" in error_msg
+            or "indexing" in error_msg
+            or "positional arguments" in error_msg
+        )
+
+    # Valid templates - Python's Formatter accepts non-identifier field names
+    valid_templates = [
+        (
+            "Hello {name} and {user_id}",
+            {"name": "Alice", "user_id": "123"},
+            "Hello Alice and 123",
+        ),
+        ("User: {user-name}", {"user-name": "Bob"}, "User: Bob"),  # Hyphen allowed
+        (
+            "Value: {2fast}",
+            {"2fast": "Charlie"},
+            "Value: Charlie",
+        ),  # Starts with digit allowed
+        ("Data: {my var}", {"my var": "Dave"}, "Data: Dave"),  # Space allowed
+    ]
+
+    for template_str, kwargs, expected in valid_templates:
+        template = ChatPromptTemplate.from_messages(
+            [("human", template_str)],
+            template_format="f-string",
+        )
+        result = template.invoke(kwargs)
+        assert result.messages[0].content == expected  # type: ignore[attr-defined]
+
+
+def test_mustache_template_attribute_access_vulnerability() -> None:
+    """Test that Mustache template injection is blocked.
+
+    Verify the fix for security vulnerability GHSA-6qv9-48xg-fc7f
+
+    Previously, Mustache used getattr() as a fallback, allowing access to
+    dangerous attributes like __class__, __globals__, etc.
+
+    The fix adds isinstance checks that reject non-dict/list types.
+    When templates try to traverse Python objects, they get empty string
+    per Mustache spec (better than the previous behavior of exposing internals).
+    """
+    msg = HumanMessage("howdy")
+
+    # Template tries to access attributes on a Python object
+    prompt = ChatPromptTemplate.from_messages(
+        [("human", "{{question.__class__.__name__}}")],
+        template_format="mustache",
+    )
+
+    # After the fix: returns empty string (attack blocked!)
+    # Previously would return "HumanMessage" via getattr()
+    result = prompt.invoke({"question": msg})
+    assert result.messages[0].content == ""  # type: ignore[attr-defined]
+
+    # Mustache still works correctly with actual dicts
+    prompt_dict = ChatPromptTemplate.from_messages(
+        [("human", "{{person.name}}")],
+        template_format="mustache",
+    )
+    result_dict = prompt_dict.invoke({"person": {"name": "Alice"}})
+    assert result_dict.messages[0].content == "Alice"  # type: ignore[attr-defined]
+
+
+@pytest.mark.requires("jinja2")
+def test_jinja2_template_attribute_access_is_blocked() -> None:
+    """Test that Jinja2 SandboxedEnvironment blocks dangerous attribute access.
+
+    This test verifies that Jinja2's sandbox successfully blocks access to
+    dangerous dunder attributes like __class__, unlike Mustache.
+
+    GOOD: Jinja2 SandboxedEnvironment raises SecurityError when attempting
+    to access __class__, __globals__, etc. This is expected behavior.
+    """
+    msg = HumanMessage("howdy")
+
+    # Create a Jinja2 template that attempts to access __class__.__name__
+    prompt = ChatPromptTemplate.from_messages(
+        [("human", "{{question.__class__.__name__}}")],
+        template_format="jinja2",
+    )
+
+    # Jinja2 sandbox should block this with SecurityError
+    with pytest.raises(Exception, match="attribute") as exc_info:
+        prompt.invoke(
+            {"question": msg, "question.__class__.__name__": "safe_placeholder"}
+        )
+
+    # Verify it's a SecurityError from Jinja2 blocking __class__ access
+    error_msg = str(exc_info.value)
+    assert (
+        "SecurityError" in str(type(exc_info.value))
+        or "access to attribute '__class__'" in error_msg
+    ), f"Expected SecurityError blocking __class__, got: {error_msg}"
+
+
+@pytest.mark.requires("jinja2")
+def test_jinja2_blocks_all_attribute_access() -> None:
+    """Test that Jinja2 now blocks ALL attribute/method access for security.
+
+    After the fix, Jinja2 uses _RestrictedSandboxedEnvironment which blocks
+    ALL attribute access, not just dunder attributes. This prevents the
+    parse_raw() vulnerability.
+    """
+    msg = HumanMessage("test content")
+
+    # Test 1: Simple variable access should still work
+    prompt_simple = ChatPromptTemplate.from_messages(
+        [("human", "Message: {{message}}")],
+        template_format="jinja2",
+    )
+    result = prompt_simple.invoke({"message": "hello world"})
+    assert "hello world" in result.messages[0].content  # type: ignore[attr-defined]
+
+    # Test 2: Attribute access should now be blocked (including safe attributes)
+    prompt_attr = ChatPromptTemplate.from_messages(
+        [("human", "Content: {{msg.content}}")],
+        template_format="jinja2",
+    )
+    with pytest.raises(Exception, match="attribute") as exc_info:
+        prompt_attr.invoke({"msg": msg})
+
+    error_msg = str(exc_info.value)
+    assert (
+        "SecurityError" in str(type(exc_info.value))
+        or "Access to attributes is not allowed" in error_msg
+    ), f"Expected SecurityError blocking attribute access, got: {error_msg}"

libs/core/tests/unit_tests/prompts/test_structured.py

@@ -125,7 +125,9 @@ def test_structured_prompt_kwargs() -> None:
 
 def test_structured_prompt_template_format() -> None:
     prompt = StructuredPrompt(
-        [("human", "hi {{person.name}}")], schema={}, template_format="mustache"
+        [("human", "hi {{person.name}}")],
+        schema={"type": "object", "properties": {}, "title": "foo"},
+        template_format="mustache",
     )
     assert prompt.messages[0].prompt.template_format == "mustache"  # type: ignore[union-attr, union-attr]
     assert prompt.input_variables == ["person"]
@@ -136,4 +138,8 @@ def test_structured_prompt_template_format() -> None:
 
 def test_structured_prompt_template_empty_vars() -> None:
     with pytest.raises(ChevronError, match="empty tag"):
-        StructuredPrompt([("human", "hi {{}}")], schema={}, template_format="mustache")
+        StructuredPrompt(
+            [("human", "hi {{}}")],
+            schema={"type": "object", "properties": {}, "title": "foo"},
+            template_format="mustache",
+        )

libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr

@@ -1744,9 +1744,11 @@
                     ```
                 
                 !!! warning "Behavior changed in `langchain-core` 0.3.9"
+                
                     Added `input_token_details` and `output_token_details`.
                 
                 !!! note "LangSmith SDK"
+                
                     The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
                     LangSmith's `UsageMetadata` has additional fields to capture cost information
                     used by the LangSmith platform.

libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr

@@ -3260,9 +3260,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -4736,9 +4738,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -6224,9 +6228,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -7568,9 +7574,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -9086,9 +9094,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -10475,9 +10485,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -11912,9 +11924,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.
@@ -13350,9 +13364,11 @@
               ```
           
           !!! warning "Behavior changed in `langchain-core` 0.3.9"
+          
               Added `input_token_details` and `output_token_details`.
           
           !!! note "LangSmith SDK"
+          
               The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
               LangSmith's `UsageMetadata` has additional fields to capture cost information
               used by the LangSmith platform.

libs/core/tests/unit_tests/runnables/test_concurrency.py

@@ -3,12 +3,14 @@
 import asyncio
 import time
 from threading import Lock
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import pytest
 
 from langchain_core.runnables import RunnableConfig, RunnableLambda
-from langchain_core.runnables.base import Runnable
+
+if TYPE_CHECKING:
+    from langchain_core.runnables.base import Runnable
 
 
 @pytest.mark.asyncio

libs/core/tests/unit_tests/runnables/test_tracing_interops.py

@@ -3,21 +3,25 @@ from __future__ import annotations
 import json
 import sys
 import uuid
-from collections.abc import AsyncGenerator, Callable, Coroutine, Generator
 from inspect import isasyncgenfunction
-from typing import Any, Literal
+from typing import TYPE_CHECKING, Any, Literal
 from unittest.mock import MagicMock, patch
 
 import pytest
 from langsmith import Client, get_current_run_tree, traceable
 from langsmith.run_helpers import tracing_context
-from langsmith.run_trees import RunTree
 from langsmith.utils import get_env_var
 
-from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.runnables.base import RunnableLambda, RunnableParallel
 from langchain_core.tracers.langchain import LangChainTracer
 
+if TYPE_CHECKING:
+    from collections.abc import AsyncGenerator, Callable, Coroutine, Generator
+
+    from langsmith.run_trees import RunTree
+
+    from langchain_core.callbacks import BaseCallbackHandler
+
 
 def _get_posts(client: Client) -> list:
     mock_calls = client.session.request.mock_calls  # type: ignore[attr-defined]

libs/core/tests/unit_tests/test_tools.py

@@ -6,6 +6,7 @@ import sys
 import textwrap
 import threading
 from collections.abc import Callable
+from dataclasses import dataclass
 from datetime import datetime
 from enum import Enum
 from functools import partial
@@ -55,6 +56,7 @@ from langchain_core.tools.base import (
     InjectedToolArg,
     InjectedToolCallId,
     SchemaAnnotationError,
+    _DirectlyInjectedToolArg,
     _is_message_content_block,
     _is_message_content_type,
     get_all_basemodel_annotations,
@@ -2331,6 +2333,101 @@ def test_injected_arg_with_complex_type() -> None:
     assert injected_tool.invoke({"x": 5, "foo": Foo()}) == "bar"
 
 
+@pytest.mark.parametrize("schema_format", ["model", "json_schema"])
+def test_tool_allows_extra_runtime_args_with_custom_schema(
+    schema_format: Literal["model", "json_schema"],
+) -> None:
+    """Ensure runtime args are preserved even if not in the args schema."""
+
+    class InputSchema(BaseModel):
+        query: str
+
+    captured: dict[str, Any] = {}
+
+    @dataclass
+    class MyRuntime(_DirectlyInjectedToolArg):
+        some_obj: object
+
+    args_schema = (
+        InputSchema if schema_format == "model" else InputSchema.model_json_schema()
+    )
+
+    @tool(args_schema=args_schema)
+    def runtime_tool(query: str, runtime: MyRuntime) -> str:
+        """Echo the query and capture runtime value."""
+        captured["runtime"] = runtime
+        return query
+
+    runtime_obj = object()
+    runtime = MyRuntime(some_obj=runtime_obj)
+    assert runtime_tool.invoke({"query": "hello", "runtime": runtime}) == "hello"
+    assert captured["runtime"] is runtime
+
+
+def test_tool_injected_tool_call_id_with_custom_schema() -> None:
+    """Ensure InjectedToolCallId works with custom args schema."""
+
+    class InputSchema(BaseModel):
+        x: int
+
+    @tool(args_schema=InputSchema)
+    def injected_tool(
+        x: int, tool_call_id: Annotated[str, InjectedToolCallId]
+    ) -> ToolMessage:
+        """Tool with injected tool_call_id and custom schema."""
+        return ToolMessage(str(x), tool_call_id=tool_call_id)
+
+    # Test that tool_call_id is properly injected even though not in custom schema
+    result = injected_tool.invoke(
+        {
+            "type": "tool_call",
+            "args": {"x": 42},
+            "name": "injected_tool",
+            "id": "test_call_id",
+        }
+    )
+    assert result == ToolMessage("42", tool_call_id="test_call_id")
+
+    # Test that it still raises error when invoked without a ToolCall
+    with pytest.raises(
+        ValueError,
+        match="When tool includes an InjectedToolCallId argument, "
+        "tool must always be invoked with a full model ToolCall",
+    ):
+        injected_tool.invoke({"x": 42})
+
+
+def test_tool_injected_arg_with_custom_schema() -> None:
+    """Ensure InjectedToolArg works with custom args schema."""
+
+    class InputSchema(BaseModel):
+        query: str
+
+    class CustomContext:
+        """Custom context object to be injected."""
+
+        def __init__(self, value: str) -> None:
+            self.value = value
+
+    captured: dict[str, Any] = {}
+
+    @tool(args_schema=InputSchema)
+    def search_tool(
+        query: str, context: Annotated[CustomContext, InjectedToolArg]
+    ) -> str:
+        """Search with custom context."""
+        captured["context"] = context
+        return f"Results for {query} with context {context.value}"
+
+    # Test that context is properly injected even though not in custom schema
+    ctx = CustomContext("test_context")
+    result = search_tool.invoke({"query": "hello", "context": ctx})
+
+    assert result == "Results for hello with context test_context"
+    assert captured["context"] is ctx
+    assert captured["context"].value == "test_context"
+
+
 def test_tool_injected_tool_call_id() -> None:
     @tool
     def foo(x: int, tool_call_id: Annotated[str, InjectedToolCallId]) -> ToolMessage:

libs/core/tests/unit_tests/utils/test_utils.py

@@ -1,10 +1,9 @@
 import os
 import re
 import sys
-from collections.abc import Callable
 from contextlib import AbstractContextManager, nullcontext
 from copy import deepcopy
-from typing import Any
+from typing import TYPE_CHECKING, Any
 from unittest.mock import patch
 
 import pytest
@@ -23,6 +22,9 @@ from langchain_core.utils import (
 from langchain_core.utils._merge import merge_dicts
 from langchain_core.utils.utils import secret_from_env
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
 
 @pytest.mark.parametrize(
     ("package", "check_kwargs", "actual_version", "expected"),

libs/core/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10.0, <4.0.0"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_python_implementation == 'PyPy'",
@@ -960,7 +960,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.0.4"
+version = "1.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "jsonpatch" },
@@ -985,7 +985,6 @@ test = [
     { name = "blockbuster" },
     { name = "freezegun" },
     { name = "grandalf" },
-    { name = "langchain-model-profiles" },
     { name = "langchain-tests" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
@@ -1001,7 +1000,6 @@ test = [
     { name = "syrupy" },
 ]
 typing = [
-    { name = "langchain-model-profiles" },
     { name = "langchain-text-splitters" },
     { name = "mypy" },
     { name = "types-pyyaml" },
@@ -1031,7 +1029,6 @@ test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
     { name = "grandalf", specifier = ">=0.8.0,<1.0.0" },
-    { name = "langchain-model-profiles", directory = "../model-profiles" },
     { name = "langchain-tests", directory = "../standard-tests" },
     { name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.4" },
     { name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
@@ -1048,56 +1045,15 @@ test = [
 ]
 test-integration = []
 typing = [
-    { name = "langchain-model-profiles", directory = "../model-profiles" },
     { name = "langchain-text-splitters", directory = "../text-splitters" },
     { name = "mypy", specifier = ">=1.18.1,<1.19.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
     { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" },
 ]
 
-[[package]]
-name = "langchain-model-profiles"
-version = "0.0.3"
-source = { directory = "../model-profiles" }
-dependencies = [
-    { name = "tomli", marker = "python_full_version < '3.11'" },
-    { name = "typing-extensions" },
-]
-
-[package.metadata]
-requires-dist = [
-    { name = "tomli", marker = "python_full_version < '3.11'", specifier = ">=2.0.0,<3.0.0" },
-    { name = "typing-extensions", specifier = ">=4.7.0,<5.0.0" },
-]
-
-[package.metadata.requires-dev]
-dev = [{ name = "httpx", specifier = ">=0.23.0,<1" }]
-lint = [
-    { name = "langchain", editable = "../langchain_v1" },
-    { name = "ruff", specifier = ">=0.12.2,<0.13.0" },
-]
-test = [
-    { name = "langchain", extras = ["openai"], editable = "../langchain_v1" },
-    { name = "langchain-core", editable = "." },
-    { name = "pytest", specifier = ">=8.0.0,<9.0.0" },
-    { name = "pytest-asyncio", specifier = ">=0.23.2,<2.0.0" },
-    { name = "pytest-cov", specifier = ">=4.0.0,<8.0.0" },
-    { name = "pytest-mock" },
-    { name = "pytest-socket", specifier = ">=0.6.0,<1.0.0" },
-    { name = "pytest-watcher", specifier = ">=0.2.6,<1.0.0" },
-    { name = "pytest-xdist", specifier = ">=3.6.1,<4.0.0" },
-    { name = "syrupy", specifier = ">=4.0.2,<5.0.0" },
-    { name = "toml", specifier = ">=0.10.2,<1.0.0" },
-]
-test-integration = [{ name = "langchain-core", editable = "." }]
-typing = [
-    { name = "mypy", specifier = ">=1.18.1,<1.19.0" },
-    { name = "types-toml", specifier = ">=0.10.8.20240310,<1.0.0.0" },
-]
-
 [[package]]
 name = "langchain-tests"
-version = "1.0.1"
+version = "1.0.2"
 source = { directory = "../standard-tests" }
 dependencies = [
     { name = "httpx" },

libs/langchain/README.md

@@ -24,7 +24,7 @@ In most cases, you should be using the main [`langchain`](https://pypi.org/proje
 
 ## 📖 Documentation
 
-For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_classic).
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_classic). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview).
 
 ## 📕 Releases & Versioning
 

libs/langchain/langchain_classic/chat_models/base.py

@@ -319,9 +319,11 @@ def init_chat_model(
         ```
 
     !!! warning "Behavior changed in `langchain` 0.2.8"
+
         Support for `configurable_fields` and `config_prefix` added.
 
     !!! warning "Behavior changed in `langchain` 0.2.12"
+
         Support for Ollama via langchain-ollama package added
         (`langchain_ollama.ChatOllama`). Previously,
         the now-deprecated langchain-community version of Ollama was imported
@@ -331,9 +333,11 @@ def init_chat_model(
         (`model_provider="bedrock_converse"`).
 
     !!! warning "Behavior changed in `langchain` 0.3.5"
+
         Out of beta.
 
     !!! warning "Behavior changed in `langchain` 0.3.19"
+
         Support for Deepseek, IBM, Nvidia, and xAI models added.
 
     """  # noqa: E501
@@ -547,13 +551,17 @@ def _attempt_infer_model_provider(model_name: str) -> str | None:
 
 
 def _parse_model(model: str, model_provider: str | None) -> tuple[str, str]:
-    if (
-        not model_provider
-        and ":" in model
-        and model.split(":")[0] in _SUPPORTED_PROVIDERS
-    ):
-        model_provider = model.split(":")[0]
-        model = ":".join(model.split(":")[1:])
+    if not model_provider and ":" in model:
+        prefix, suffix = model.split(":", 1)
+        if prefix in _SUPPORTED_PROVIDERS:
+            model_provider = prefix
+            model = suffix
+        else:
+            inferred = _attempt_infer_model_provider(prefix)
+            if inferred:
+                model_provider = inferred
+                model = suffix
+
     model_provider = model_provider or _attempt_infer_model_provider(model)
     if not model_provider:
         msg = (

libs/langchain/langchain_classic/retrievers/multi_vector.py

@@ -32,13 +32,18 @@ class MultiVectorRetriever(BaseRetriever):
     vectorstore: VectorStore
     """The underlying `VectorStore` to use to store small chunks
     and their embedding vectors"""
+
     byte_store: ByteStore | None = None
     """The lower-level backing storage layer for the parent documents"""
+
     docstore: BaseStore[str, Document]
     """The storage interface for the parent documents"""
+
     id_key: str = "doc_id"
+
     search_kwargs: dict = Field(default_factory=dict)
     """Keyword arguments to pass to the search function."""
+
     search_type: SearchType = SearchType.similarity
     """Type of search to perform (similarity / mmr)"""
 

libs/langchain/langchain_classic/smith/evaluation/progress.py

@@ -26,7 +26,6 @@ class ProgressBarCallback(base_callbacks.BaseCallbackHandler):
             total: The total number of items to be processed.
             ncols: The character width of the progress bar.
             end_with: Last string to print after progress bar reaches end.
-            **kwargs: Additional keyword arguments.
         """
         self.total = total
         self.ncols = ncols

libs/langchain/langchain_classic/smith/evaluation/runner_utils.py

@@ -295,11 +295,7 @@ def _get_prompt(inputs: dict[str, Any]) -> str:
 
 
 class ChatModelInput(TypedDict):
-    """Input for a chat model.
-
-    Args:
-        messages: List of chat messages.
-    """
+    """Input for a chat model."""
 
     messages: list[BaseMessage]
 

libs/langchain/langchain_classic/smith/evaluation/string_run_evaluator.py

@@ -108,8 +108,8 @@ class LLMStringRunMapper(StringRunMapper):
             The serialized output text from the first generation.
 
         Raises:
-            ValueError: If no generations are found in the outputs,
-            or if the generations are empty.
+            ValueError: If no generations are found in the outputs or if the generations
+                are empty.
         """
         if not outputs.get("generations"):
             msg = "Cannot evaluate LLM Run without generations."
@@ -436,8 +436,8 @@ class StringRunEvaluatorChain(Chain, RunEvaluator):
             The instantiated evaluation chain.
 
         Raises:
-            If the run type is not supported, or if the evaluator requires a
-            reference from the dataset but the reference key is not provided.
+            ValueError: If the run type is not supported, or if the evaluator requires a
+                reference from the dataset but the reference key is not provided.
 
         """
         # Configure how run inputs/predictions are passed to the evaluator

libs/langchain_v1/Makefile

@@ -1,4 +1,4 @@
-.PHONY: all start_services stop_services coverage test test_fast extended_tests test_watch test_watch_extended integration_tests check_imports lint format lint_diff format_diff lint_package lint_tests help
+.PHONY: all start_services stop_services coverage coverage_agents test test_fast extended_tests test_watch test_watch_extended integration_tests check_imports lint format lint_diff format_diff lint_package lint_tests help
 
 # Default target executed when no arguments are given to make.
 all: help
@@ -27,8 +27,17 @@ coverage:
 		--cov-report term-missing:skip-covered \
 		$(TEST_FILE)
 
+# Run middleware and agent tests with coverage report.
+coverage_agents:
+	uv run --group test pytest \
+		tests/unit_tests/agents/middleware/ \
+		tests/unit_tests/agents/test_*.py \
+		--cov=langchain.agents \
+		--cov-report=term-missing \
+		--cov-report=html:htmlcov \
+
 test:
-	make start_services && LANGGRAPH_TEST_FAST=0 uv run --no-sync --active --group test pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE) --cov-report term-missing:skip-covered; \
+	make start_services && LANGGRAPH_TEST_FAST=0 uv run --no-sync --active --group test pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE) --cov-report term-missing:skip-covered --snapshot-update; \
 	EXIT_CODE=$$?; \
 	make stop_services; \
 	exit $$EXIT_CODE
@@ -93,6 +102,7 @@ help:
 	@echo 'lint                         - run linters'
 	@echo '-- TESTS --'
 	@echo 'coverage                     - run unit tests and generate coverage report'
+	@echo 'coverage_agents              - run middleware and agent tests with coverage report'
 	@echo 'test                         - run unit tests with all services'
 	@echo 'test_fast                    - run unit tests with in-memory services only'
 	@echo 'tests                        - run unit tests (alias for "make test")'

libs/langchain_v1/README.md

@@ -26,7 +26,7 @@ LangChain [agents](https://docs.langchain.com/oss/python/langchain/agents) are b
 
 ## 📖 Documentation
 
-For full documentation, see the [API reference](https://reference.langchain.com/python/langchain/langchain/).
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain/langchain/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview).
 
 ## 📕 Releases & Versioning
 

libs/langchain_v1/langchain/__init__.py

@@ -1,3 +1,3 @@
 """Main entrypoint into LangChain."""
 
-__version__ = "1.0.5"
+__version__ = "1.1.0"

libs/langchain_v1/langchain/agents/factory.py

@@ -63,6 +63,18 @@ if TYPE_CHECKING:
 
 STRUCTURED_OUTPUT_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
 
+FALLBACK_MODELS_WITH_STRUCTURED_OUTPUT = [
+    # if model profile data are not available, these models are assumed to support
+    # structured output
+    "grok",
+    "gpt-5",
+    "gpt-4.1",
+    "gpt-4o",
+    "gpt-oss",
+    "o3-pro",
+    "o3-mini",
+]
+
 
 def _normalize_to_model_response(result: ModelResponse | AIMessage) -> ModelResponse:
     """Normalize middleware return value to ModelResponse."""
@@ -349,11 +361,13 @@ def _get_can_jump_to(middleware: AgentMiddleware[Any, Any], hook_name: str) -> l
     return []
 
 
-def _supports_provider_strategy(model: str | BaseChatModel) -> bool:
+def _supports_provider_strategy(model: str | BaseChatModel, tools: list | None = None) -> bool:
     """Check if a model supports provider-specific structured output.
 
     Args:
         model: Model name string or `BaseChatModel` instance.
+        tools: Optional list of tools provided to the agent. Needed because some models
+            don't support structured output together with tool calling.
 
     Returns:
         `True` if the model supports provider-specific structured output, `False` otherwise.
@@ -362,11 +376,23 @@ def _supports_provider_strategy(model: str | BaseChatModel) -> bool:
     if isinstance(model, str):
         model_name = model
     elif isinstance(model, BaseChatModel):
-        model_name = getattr(model, "model_name", None)
+        model_name = (
+            getattr(model, "model_name", None)
+            or getattr(model, "model", None)
+            or getattr(model, "model_id", "")
+        )
+        model_profile = model.profile
+        if (
+            model_profile is not None
+            and model_profile.get("structured_output")
+            # We make an exception for Gemini models, which currently do not support
+            # simultaneous tool use with structured output
+            and not (tools and isinstance(model_name, str) and "gemini" in model_name.lower())
+        ):
+            return True
 
     return (
-        "grok" in model_name.lower()
-        or any(part in model_name for part in ["gpt-5", "gpt-4.1", "gpt-oss", "o3-pro", "o3-mini"])
+        any(part in model_name.lower() for part in FALLBACK_MODELS_WITH_STRUCTURED_OUTPUT)
         if model_name
         else False
     )
@@ -516,7 +542,7 @@ def create_agent(  # noqa: PLR0915
     model: str | BaseChatModel,
     tools: Sequence[BaseTool | Callable | dict[str, Any]] | None = None,
     *,
-    system_prompt: str | None = None,
+    system_prompt: str | SystemMessage | None = None,
     middleware: Sequence[AgentMiddleware[StateT_co, ContextT]] = (),
     response_format: ResponseFormat[ResponseT] | type[ResponseT] | None = None,
     state_schema: type[AgentState[ResponseT]] | None = None,
@@ -562,9 +588,9 @@ def create_agent(  # noqa: PLR0915
                 docs for more information.
         system_prompt: An optional system prompt for the LLM.
 
-            Prompts are converted to a
-            [`SystemMessage`][langchain.messages.SystemMessage] and added to the
-            beginning of the message list.
+            Can be a `str` (which will be converted to a `SystemMessage`) or a
+            `SystemMessage` instance directly. The system message is added to the
+            beginning of the message list when calling the model.
         middleware: A sequence of middleware instances to apply to the agent.
 
             Middleware can intercept and modify agent behavior at various stages.
@@ -659,6 +685,14 @@ def create_agent(  # noqa: PLR0915
     if isinstance(model, str):
         model = init_chat_model(model)
 
+    # Convert system_prompt to SystemMessage if needed
+    system_message: SystemMessage | None = None
+    if system_prompt is not None:
+        if isinstance(system_prompt, SystemMessage):
+            system_message = system_prompt
+        else:
+            system_message = SystemMessage(content=system_prompt)
+
     # Handle tools being None or empty
     if tools is None:
         tools = []
@@ -988,7 +1022,7 @@ def create_agent(  # noqa: PLR0915
         effective_response_format: ResponseFormat | None
         if isinstance(request.response_format, AutoStrategy):
             # User provided raw schema via AutoStrategy - auto-detect best strategy based on model
-            if _supports_provider_strategy(request.model):
+            if _supports_provider_strategy(request.model, tools=request.tools):
                 # Model supports provider strategy - use it
                 effective_response_format = ProviderStrategy(schema=request.response_format.schema)
             else:
@@ -1009,7 +1043,7 @@ def create_agent(  # noqa: PLR0915
 
         # Bind model based on effective response format
         if isinstance(effective_response_format, ProviderStrategy):
-            # Use provider-specific structured output
+            # (Backward compatibility) Use OpenAI format structured output
             kwargs = effective_response_format.to_model_kwargs()
             return (
                 request.model.bind_tools(
@@ -1062,8 +1096,8 @@ def create_agent(  # noqa: PLR0915
         # Get the bound model (with auto-detection if needed)
         model_, effective_response_format = _get_bound_model(request)
         messages = request.messages
-        if request.system_prompt:
-            messages = [SystemMessage(request.system_prompt), *messages]
+        if request.system_message:
+            messages = [request.system_message, *messages]
 
         output = model_.invoke(messages)
 
@@ -1082,7 +1116,7 @@ def create_agent(  # noqa: PLR0915
         request = ModelRequest(
             model=model,
             tools=default_tools,
-            system_prompt=system_prompt,
+            system_message=system_message,
             response_format=initial_response_format,
             messages=state["messages"],
             tool_choice=None,
@@ -1115,8 +1149,8 @@ def create_agent(  # noqa: PLR0915
         # Get the bound model (with auto-detection if needed)
         model_, effective_response_format = _get_bound_model(request)
         messages = request.messages
-        if request.system_prompt:
-            messages = [SystemMessage(request.system_prompt), *messages]
+        if request.system_message:
+            messages = [request.system_message, *messages]
 
         output = await model_.ainvoke(messages)
 
@@ -1135,7 +1169,7 @@ def create_agent(  # noqa: PLR0915
         request = ModelRequest(
             model=model,
             tools=default_tools,
-            system_prompt=system_prompt,
+            system_message=system_message,
             response_format=initial_response_format,
             messages=state["messages"],
             tool_choice=None,

libs/langchain_v1/langchain/agents/middleware/__init__.py

@@ -11,6 +11,7 @@ from .human_in_the_loop import (
 )
 from .model_call_limit import ModelCallLimitMiddleware
 from .model_fallback import ModelFallbackMiddleware
+from .model_retry import ModelRetryMiddleware
 from .pii import PIIDetectionError, PIIMiddleware
 from .shell_tool import (
     CodexSandboxExecutionPolicy,
@@ -57,6 +58,7 @@ __all__ = [
     "ModelFallbackMiddleware",
     "ModelRequest",
     "ModelResponse",
+    "ModelRetryMiddleware",
     "PIIDetectionError",
     "PIIMiddleware",
     "RedactionRule",

libs/langchain_v1/langchain/agents/middleware/_retry.py

@@ -0,0 +1,123 @@
+"""Shared retry utilities for agent middleware.
+
+This module contains common constants, utilities, and logic used by both
+model and tool retry middleware implementations.
+"""
+
+from __future__ import annotations
+
+import random
+from collections.abc import Callable
+from typing import Literal
+
+# Type aliases
+RetryOn = tuple[type[Exception], ...] | Callable[[Exception], bool]
+"""Type for specifying which exceptions to retry on.
+
+Can be either:
+- A tuple of exception types to retry on (based on `isinstance` checks)
+- A callable that takes an exception and returns `True` if it should be retried
+"""
+
+OnFailure = Literal["error", "continue"] | Callable[[Exception], str]
+"""Type for specifying failure handling behavior.
+
+Can be either:
+- A literal action string (`'error'` or `'continue'`)
+    - `'error'`: Re-raise the exception, stopping agent execution.
+    - `'continue'`: Inject a message with the error details, allowing the agent to continue.
+       For tool retries, a `ToolMessage` with the error details will be injected.
+       For model retries, an `AIMessage` with the error details will be returned.
+- A callable that takes an exception and returns a string for error message content
+"""
+
+
+def validate_retry_params(
+    max_retries: int,
+    initial_delay: float,
+    max_delay: float,
+    backoff_factor: float,
+) -> None:
+    """Validate retry parameters.
+
+    Args:
+        max_retries: Maximum number of retry attempts.
+        initial_delay: Initial delay in seconds before first retry.
+        max_delay: Maximum delay in seconds between retries.
+        backoff_factor: Multiplier for exponential backoff.
+
+    Raises:
+        ValueError: If any parameter is invalid (negative values).
+    """
+    if max_retries < 0:
+        msg = "max_retries must be >= 0"
+        raise ValueError(msg)
+    if initial_delay < 0:
+        msg = "initial_delay must be >= 0"
+        raise ValueError(msg)
+    if max_delay < 0:
+        msg = "max_delay must be >= 0"
+        raise ValueError(msg)
+    if backoff_factor < 0:
+        msg = "backoff_factor must be >= 0"
+        raise ValueError(msg)
+
+
+def should_retry_exception(
+    exc: Exception,
+    retry_on: RetryOn,
+) -> bool:
+    """Check if an exception should trigger a retry.
+
+    Args:
+        exc: The exception that occurred.
+        retry_on: Either a tuple of exception types to retry on, or a callable
+            that takes an exception and returns `True` if it should be retried.
+
+    Returns:
+        `True` if the exception should be retried, `False` otherwise.
+    """
+    if callable(retry_on):
+        return retry_on(exc)
+    return isinstance(exc, retry_on)
+
+
+def calculate_delay(
+    retry_number: int,
+    *,
+    backoff_factor: float,
+    initial_delay: float,
+    max_delay: float,
+    jitter: bool,
+) -> float:
+    """Calculate delay for a retry attempt with exponential backoff and optional jitter.
+
+    Args:
+        retry_number: The retry attempt number (0-indexed).
+        backoff_factor: Multiplier for exponential backoff.
+
+            Set to `0.0` for constant delay.
+        initial_delay: Initial delay in seconds before first retry.
+        max_delay: Maximum delay in seconds between retries.
+
+            Caps exponential backoff growth.
+        jitter: Whether to add random jitter to delay to avoid thundering herd.
+
+    Returns:
+        Delay in seconds before next retry.
+    """
+    if backoff_factor == 0.0:
+        delay = initial_delay
+    else:
+        delay = initial_delay * (backoff_factor**retry_number)
+
+    # Cap at max_delay
+    delay = min(delay, max_delay)
+
+    if jitter and delay > 0:
+        jitter_amount = delay * 0.25  # ±25% jitter
+        delay = delay + random.uniform(-jitter_amount, jitter_amount)  # noqa: S311
+        # Ensure delay is not negative after jitter
+        delay = max(0, delay)
+
+    return delay

libs/langchain_v1/langchain/agents/middleware/context_editing.py

@@ -10,6 +10,7 @@ chat model.
 from __future__ import annotations
 
 from collections.abc import Awaitable, Callable, Iterable, Sequence
+from copy import deepcopy
 from dataclasses import dataclass
 from typing import Literal
 
@@ -17,7 +18,6 @@ from langchain_core.messages import (
     AIMessage,
     AnyMessage,
     BaseMessage,
-    SystemMessage,
     ToolMessage,
 )
 from langchain_core.messages.utils import count_tokens_approximately
@@ -189,7 +189,7 @@ class ContextEditingMiddleware(AgentMiddleware):
     configured thresholds.
 
     Currently the `ClearToolUsesEdit` strategy is supported, aligning with Anthropic's
-    `clear_tool_uses_20250919` behavior [(read more)](https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool).
+    `clear_tool_uses_20250919` behavior [(read more)](https://platform.claude.com/docs/en/agents-and-tools/tool-use/memory-tool).
     """
 
     edits: list[ContextEdit]
@@ -229,19 +229,18 @@ class ContextEditingMiddleware(AgentMiddleware):
             def count_tokens(messages: Sequence[BaseMessage]) -> int:
                 return count_tokens_approximately(messages)
         else:
-            system_msg = (
-                [SystemMessage(content=request.system_prompt)] if request.system_prompt else []
-            )
+            system_msg = [request.system_message] if request.system_message else []
 
             def count_tokens(messages: Sequence[BaseMessage]) -> int:
                 return request.model.get_num_tokens_from_messages(
                     system_msg + list(messages), request.tools
                 )
 
+        edited_messages = deepcopy(list(request.messages))
         for edit in self.edits:
-            edit.apply(request.messages, count_tokens=count_tokens)
+            edit.apply(edited_messages, count_tokens=count_tokens)
 
-        return handler(request)
+        return handler(request.override(messages=edited_messages))
 
     async def awrap_model_call(
         self,
@@ -257,19 +256,18 @@ class ContextEditingMiddleware(AgentMiddleware):
             def count_tokens(messages: Sequence[BaseMessage]) -> int:
                 return count_tokens_approximately(messages)
         else:
-            system_msg = (
-                [SystemMessage(content=request.system_prompt)] if request.system_prompt else []
-            )
+            system_msg = [request.system_message] if request.system_message else []
 
             def count_tokens(messages: Sequence[BaseMessage]) -> int:
                 return request.model.get_num_tokens_from_messages(
                     system_msg + list(messages), request.tools
                 )
 
+        edited_messages = deepcopy(list(request.messages))
         for edit in self.edits:
-            edit.apply(request.messages, count_tokens=count_tokens)
+            edit.apply(edited_messages, count_tokens=count_tokens)
 
-        return await handler(request)
+        return await handler(request.override(messages=edited_messages))
 
 
 __all__ = [

libs/langchain_v1/langchain/agents/middleware/file_search.py

@@ -120,9 +120,9 @@ class FilesystemFileSearchMiddleware(AgentMiddleware):
 
         Args:
             root_path: Root directory to search.
-            use_ripgrep: Whether to use ripgrep for search.
+            use_ripgrep: Whether to use `ripgrep` for search.
 
-                Falls back to Python if ripgrep unavailable.
+                Falls back to Python if `ripgrep` unavailable.
             max_file_size_mb: Maximum file size to search in MB.
         """
         self.root_path = Path(root_path).resolve()

libs/langchain_v1/langchain/agents/middleware/human_in_the_loop.py

@@ -7,7 +7,7 @@ from langgraph.runtime import Runtime
 from langgraph.types import interrupt
 from typing_extensions import NotRequired, TypedDict
 
-from langchain.agents.middleware.types import AgentMiddleware, AgentState
+from langchain.agents.middleware.types import AgentMiddleware, AgentState, ContextT, StateT
 
 
 class Action(TypedDict):
@@ -102,7 +102,7 @@ class HITLResponse(TypedDict):
 class _DescriptionFactory(Protocol):
     """Callable that generates a description for a tool call."""
 
-    def __call__(self, tool_call: ToolCall, state: AgentState, runtime: Runtime) -> str:
+    def __call__(self, tool_call: ToolCall, state: AgentState, runtime: Runtime[ContextT]) -> str:
         """Generate a description for a tool call."""
         ...
 
@@ -138,7 +138,7 @@ class InterruptOnConfig(TypedDict):
         def format_tool_description(
             tool_call: ToolCall,
             state: AgentState,
-            runtime: Runtime
+            runtime: Runtime[ContextT]
         ) -> str:
             import json
             return (
@@ -156,7 +156,7 @@ class InterruptOnConfig(TypedDict):
     """JSON schema for the args associated with the action, if edits are allowed."""
 
 
-class HumanInTheLoopMiddleware(AgentMiddleware):
+class HumanInTheLoopMiddleware(AgentMiddleware[StateT, ContextT]):
     """Human in the loop middleware."""
 
     def __init__(
@@ -204,7 +204,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         tool_call: ToolCall,
         config: InterruptOnConfig,
         state: AgentState,
-        runtime: Runtime,
+        runtime: Runtime[ContextT],
     ) -> tuple[ActionRequest, ReviewConfig]:
         """Create an ActionRequest and ReviewConfig for a tool call."""
         tool_name = tool_call["name"]
@@ -277,7 +277,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         )
         raise ValueError(msg)
 
-    def after_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    def after_model(self, state: AgentState, runtime: Runtime[ContextT]) -> dict[str, Any] | None:
         """Trigger interrupt flows for relevant tool calls after an `AIMessage`."""
         messages = state["messages"]
         if not messages:
@@ -287,36 +287,23 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         if not last_ai_msg or not last_ai_msg.tool_calls:
             return None
 
-        # Separate tool calls that need interrupts from those that don't
-        interrupt_tool_calls: list[ToolCall] = []
-        auto_approved_tool_calls = []
-
-        for tool_call in last_ai_msg.tool_calls:
-            interrupt_tool_calls.append(tool_call) if tool_call[
-                "name"
-            ] in self.interrupt_on else auto_approved_tool_calls.append(tool_call)
-
-        # If no interrupts needed, return early
-        if not interrupt_tool_calls:
-            return None
-
-        # Process all tool calls that require interrupts
-        revised_tool_calls: list[ToolCall] = auto_approved_tool_calls.copy()
-        artificial_tool_messages: list[ToolMessage] = []
-
-        # Create action requests and review configs for all tools that need approval
+        # Create action requests and review configs for tools that need approval
         action_requests: list[ActionRequest] = []
         review_configs: list[ReviewConfig] = []
+        interrupt_indices: list[int] = []
 
-        for tool_call in interrupt_tool_calls:
-            config = self.interrupt_on[tool_call["name"]]
+        for idx, tool_call in enumerate(last_ai_msg.tool_calls):
+            if (config := self.interrupt_on.get(tool_call["name"])) is not None:
+                action_request, review_config = self._create_action_and_config(
+                    tool_call, config, state, runtime
+                )
+                action_requests.append(action_request)
+                review_configs.append(review_config)
+                interrupt_indices.append(idx)
 
-            # Create ActionRequest and ReviewConfig using helper method
-            action_request, review_config = self._create_action_and_config(
-                tool_call, config, state, runtime
-            )
-            action_requests.append(action_request)
-            review_configs.append(review_config)
+        # If no interrupts needed, return early
+        if not action_requests:
+            return None
 
         # Create single HITLRequest with all actions and configs
         hitl_request = HITLRequest(
@@ -325,35 +312,46 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         )
 
         # Send interrupt and get response
-        hitl_response: HITLResponse = interrupt(hitl_request)
-        decisions = hitl_response["decisions"]
+        decisions = interrupt(hitl_request)["decisions"]
 
         # Validate that the number of decisions matches the number of interrupt tool calls
-        if (decisions_len := len(decisions)) != (
-            interrupt_tool_calls_len := len(interrupt_tool_calls)
-        ):
+        if (decisions_len := len(decisions)) != (interrupt_count := len(interrupt_indices)):
             msg = (
                 f"Number of human decisions ({decisions_len}) does not match "
-                f"number of hanging tool calls ({interrupt_tool_calls_len})."
+                f"number of hanging tool calls ({interrupt_count})."
             )
             raise ValueError(msg)
 
-        # Process each decision using helper method
-        for i, decision in enumerate(decisions):
-            tool_call = interrupt_tool_calls[i]
-            config = self.interrupt_on[tool_call["name"]]
+        # Process decisions and rebuild tool calls in original order
+        revised_tool_calls: list[ToolCall] = []
+        artificial_tool_messages: list[ToolMessage] = []
+        decision_idx = 0
 
-            revised_tool_call, tool_message = self._process_decision(decision, tool_call, config)
-            if revised_tool_call:
-                revised_tool_calls.append(revised_tool_call)
-            if tool_message:
-                artificial_tool_messages.append(tool_message)
+        for idx, tool_call in enumerate(last_ai_msg.tool_calls):
+            if idx in interrupt_indices:
+                # This was an interrupt tool call - process the decision
+                config = self.interrupt_on[tool_call["name"]]
+                decision = decisions[decision_idx]
+                decision_idx += 1
+
+                revised_tool_call, tool_message = self._process_decision(
+                    decision, tool_call, config
+                )
+                if revised_tool_call is not None:
+                    revised_tool_calls.append(revised_tool_call)
+                if tool_message:
+                    artificial_tool_messages.append(tool_message)
+            else:
+                # This was auto-approved - keep original
+                revised_tool_calls.append(tool_call)
 
         # Update the AI message to only include approved tool calls
         last_ai_msg.tool_calls = revised_tool_calls
 
         return {"messages": [last_ai_msg, *artificial_tool_messages]}
 
-    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    async def aafter_model(
+        self, state: AgentState, runtime: Runtime[ContextT]
+    ) -> dict[str, Any] | None:
         """Async trigger interrupt flows for relevant tool calls after an `AIMessage`."""
         return self.after_model(state, runtime)

libs/langchain_v1/langchain/agents/middleware/model_call_limit.py

@@ -133,6 +133,7 @@ class ModelCallLimitMiddleware(AgentMiddleware[ModelCallLimitState, Any]):
 
                 `None` means no limit.
             exit_behavior: What to do when limits are exceeded.
+
                 - `'end'`: Jump to the end of the agent execution and
                     inject an artificial AI message indicating that the limit was
                     exceeded.

libs/langchain_v1/langchain/agents/middleware/model_fallback.py

@@ -92,9 +92,8 @@ class ModelFallbackMiddleware(AgentMiddleware):
 
         # Try fallback models
         for fallback_model in self.models:
-            request.model = fallback_model
             try:
-                return handler(request)
+                return handler(request.override(model=fallback_model))
             except Exception as e:  # noqa: BLE001
                 last_exception = e
                 continue
@@ -127,9 +126,8 @@ class ModelFallbackMiddleware(AgentMiddleware):
 
         # Try fallback models
         for fallback_model in self.models:
-            request.model = fallback_model
             try:
-                return await handler(request)
+                return await handler(request.override(model=fallback_model))
             except Exception as e:  # noqa: BLE001
                 last_exception = e
                 continue

libs/langchain_v1/langchain/agents/middleware/model_retry.py

@@ -0,0 +1,300 @@
+"""Model retry middleware for agents."""
+
+from __future__ import annotations
+
+import asyncio
+import time
+from typing import TYPE_CHECKING
+
+from langchain_core.messages import AIMessage
+
+from langchain.agents.middleware._retry import (
+    OnFailure,
+    RetryOn,
+    calculate_delay,
+    should_retry_exception,
+    validate_retry_params,
+)
+from langchain.agents.middleware.types import AgentMiddleware, ModelResponse
+
+if TYPE_CHECKING:
+    from collections.abc import Awaitable, Callable
+
+    from langchain.agents.middleware.types import ModelRequest
+
+
+class ModelRetryMiddleware(AgentMiddleware):
+    """Middleware that automatically retries failed model calls with configurable backoff.
+
+    Supports retrying on specific exceptions and exponential backoff.
+
+    Examples:
+        !!! example "Basic usage with default settings (2 retries, exponential backoff)"
+
+            ```python
+            from langchain.agents import create_agent
+            from langchain.agents.middleware import ModelRetryMiddleware
+
+            agent = create_agent(model, tools=[search_tool], middleware=[ModelRetryMiddleware()])
+            ```
+
+        !!! example "Retry specific exceptions only"
+
+            ```python
+            from anthropic import RateLimitError
+            from openai import APITimeoutError
+
+            retry = ModelRetryMiddleware(
+                max_retries=4,
+                retry_on=(APITimeoutError, RateLimitError),
+                backoff_factor=1.5,
+            )
+            ```
+
+        !!! example "Custom exception filtering"
+
+            ```python
+            from anthropic import APIStatusError
+
+
+            def should_retry(exc: Exception) -> bool:
+                # Only retry on 5xx errors
+                if isinstance(exc, APIStatusError):
+                    return 500 <= exc.status_code < 600
+                return False
+
+
+            retry = ModelRetryMiddleware(
+                max_retries=3,
+                retry_on=should_retry,
+            )
+            ```
+
+        !!! example "Custom error handling"
+
+            ```python
+            def format_error(exc: Exception) -> str:
+                return "Model temporarily unavailable. Please try again later."
+
+
+            retry = ModelRetryMiddleware(
+                max_retries=4,
+                on_failure=format_error,
+            )
+            ```
+
+        !!! example "Constant backoff (no exponential growth)"
+
+            ```python
+            retry = ModelRetryMiddleware(
+                max_retries=5,
+                backoff_factor=0.0,  # No exponential growth
+                initial_delay=2.0,  # Always wait 2 seconds
+            )
+            ```
+
+        !!! example "Raise exception on failure"
+
+            ```python
+            retry = ModelRetryMiddleware(
+                max_retries=2,
+                on_failure="error",  # Re-raise exception instead of returning message
+            )
+            ```
+    """
+
+    def __init__(
+        self,
+        *,
+        max_retries: int = 2,
+        retry_on: RetryOn = (Exception,),
+        on_failure: OnFailure = "continue",
+        backoff_factor: float = 2.0,
+        initial_delay: float = 1.0,
+        max_delay: float = 60.0,
+        jitter: bool = True,
+    ) -> None:
+        """Initialize `ModelRetryMiddleware`.
+
+        Args:
+            max_retries: Maximum number of retry attempts after the initial call.
+
+                Must be `>= 0`.
+            retry_on: Either a tuple of exception types to retry on, or a callable
+                that takes an exception and returns `True` if it should be retried.
+
+                Default is to retry on all exceptions.
+            on_failure: Behavior when all retries are exhausted.
+
+                Options:
+
+                - `'continue'`: Return an `AIMessage` with error details,
+                    allowing the agent to continue with an error response.
+                - `'error'`: Re-raise the exception, stopping agent execution.
+                - **Custom callable:** Function that takes the exception and returns a
+                    string for the `AIMessage` content, allowing custom error
+                    formatting.
+            backoff_factor: Multiplier for exponential backoff.
+
+                Each retry waits `initial_delay * (backoff_factor ** retry_number)`
+                seconds.
+
+                Set to `0.0` for constant delay.
+            initial_delay: Initial delay in seconds before first retry.
+            max_delay: Maximum delay in seconds between retries.
+
+                Caps exponential backoff growth.
+            jitter: Whether to add random jitter (`±25%`) to delay to avoid thundering herd.
+
+        Raises:
+            ValueError: If `max_retries < 0` or delays are negative.
+        """
+        super().__init__()
+
+        # Validate parameters
+        validate_retry_params(max_retries, initial_delay, max_delay, backoff_factor)
+
+        self.max_retries = max_retries
+        self.tools = []  # No additional tools registered by this middleware
+        self.retry_on = retry_on
+        self.on_failure = on_failure
+        self.backoff_factor = backoff_factor
+        self.initial_delay = initial_delay
+        self.max_delay = max_delay
+        self.jitter = jitter
+
+    def _format_failure_message(self, exc: Exception, attempts_made: int) -> AIMessage:
+        """Format the failure message when retries are exhausted.
+
+        Args:
+            exc: The exception that caused the failure.
+            attempts_made: Number of attempts actually made.
+
+        Returns:
+            `AIMessage` with formatted error message.
+        """
+        exc_type = type(exc).__name__
+        exc_msg = str(exc)
+        attempt_word = "attempt" if attempts_made == 1 else "attempts"
+        content = (
+            f"Model call failed after {attempts_made} {attempt_word} with {exc_type}: {exc_msg}"
+        )
+        return AIMessage(content=content)
+
+    def _handle_failure(self, exc: Exception, attempts_made: int) -> ModelResponse:
+        """Handle failure when all retries are exhausted.
+
+        Args:
+            exc: The exception that caused the failure.
+            attempts_made: Number of attempts actually made.
+
+        Returns:
+            `ModelResponse` with error details.
+
+        Raises:
+            Exception: If `on_failure` is `'error'`, re-raises the exception.
+        """
+        if self.on_failure == "error":
+            raise exc
+
+        if callable(self.on_failure):
+            content = self.on_failure(exc)
+            ai_msg = AIMessage(content=content)
+        else:
+            ai_msg = self._format_failure_message(exc, attempts_made)
+
+        return ModelResponse(result=[ai_msg])
+
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse | AIMessage:
+        """Intercept model execution and retry on failure.
+
+        Args:
+            request: Model request with model, messages, state, and runtime.
+            handler: Callable to execute the model (can be called multiple times).
+
+        Returns:
+            `ModelResponse` or `AIMessage` (the final result).
+        """
+        # Initial attempt + retries
+        for attempt in range(self.max_retries + 1):
+            try:
+                return handler(request)
+            except Exception as exc:  # noqa: BLE001
+                attempts_made = attempt + 1  # attempt is 0-indexed
+
+                # Check if we should retry this exception
+                if not should_retry_exception(exc, self.retry_on):
+                    # Exception is not retryable, handle failure immediately
+                    return self._handle_failure(exc, attempts_made)
+
+                # Check if we have more retries left
+                if attempt < self.max_retries:
+                    # Calculate and apply backoff delay
+                    delay = calculate_delay(
+                        attempt,
+                        backoff_factor=self.backoff_factor,
+                        initial_delay=self.initial_delay,
+                        max_delay=self.max_delay,
+                        jitter=self.jitter,
+                    )
+                    if delay > 0:
+                        time.sleep(delay)
+                    # Continue to next retry
+                else:
+                    # No more retries, handle failure
+                    return self._handle_failure(exc, attempts_made)
+
+        # Unreachable: loop always returns via handler success or _handle_failure
+        msg = "Unexpected: retry loop completed without returning"
+        raise RuntimeError(msg)
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse | AIMessage:
+        """Intercept and control async model execution with retry logic.
+
+        Args:
+            request: Model request with model, messages, state, and runtime.
+            handler: Async callable to execute the model and returns `ModelResponse`.
+
+        Returns:
+            `ModelResponse` or `AIMessage` (the final result).
+        """
+        # Initial attempt + retries
+        for attempt in range(self.max_retries + 1):
+            try:
+                return await handler(request)
+            except Exception as exc:  # noqa: BLE001
+                attempts_made = attempt + 1  # attempt is 0-indexed
+
+                # Check if we should retry this exception
+                if not should_retry_exception(exc, self.retry_on):
+                    # Exception is not retryable, handle failure immediately
+                    return self._handle_failure(exc, attempts_made)
+
+                # Check if we have more retries left
+                if attempt < self.max_retries:
+                    # Calculate and apply backoff delay
+                    delay = calculate_delay(
+                        attempt,
+                        backoff_factor=self.backoff_factor,
+                        initial_delay=self.initial_delay,
+                        max_delay=self.max_delay,
+                        jitter=self.jitter,
+                    )
+                    if delay > 0:
+                        await asyncio.sleep(delay)
+                    # Continue to next retry
+                else:
+                    # No more retries, handle failure
+                    return self._handle_failure(exc, attempts_made)
+
+        # Unreachable: loop always returns via handler success or _handle_failure
+        msg = "Unexpected: retry loop completed without returning"
+        raise RuntimeError(msg)

libs/langchain_v1/langchain/agents/middleware/shell_tool.py

@@ -15,7 +15,7 @@ import uuid
 import weakref
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import TYPE_CHECKING, Annotated, Any, Literal
+from typing import TYPE_CHECKING, Annotated, Any, Literal, cast
 
 from langchain_core.messages import ToolMessage
 from langchain_core.tools.base import ToolException
@@ -339,7 +339,7 @@ class _ShellToolInput(BaseModel):
     restart: bool | None = None
     """Whether to restart the shell session."""
 
-    runtime: Annotated[Any, SkipJsonSchema] = None
+    runtime: Annotated[Any, SkipJsonSchema()] = None
     """The runtime for the shell tool.
 
     Included as a workaround at the moment bc args_schema doesn't work with
@@ -389,7 +389,7 @@ class ShellToolMiddleware(AgentMiddleware[ShellToolState, Any]):
         shell_command: Sequence[str] | str | None = None,
         env: Mapping[str, Any] | None = None,
     ) -> None:
-        """Initialize the middleware.
+        """Initialize an instance of `ShellToolMiddleware`.
 
         Args:
             workspace_root: Base directory for the shell session.
@@ -445,7 +445,7 @@ class ShellToolMiddleware(AgentMiddleware[ShellToolState, Any]):
             command: str | None = None,
             restart: bool = False,
         ) -> ToolMessage | str:
-            resources = self._ensure_resources(runtime.state)
+            resources = self._get_or_create_resources(runtime.state)
             return self._run_shell_tool(
                 resources,
                 {"command": command, "restart": restart},
@@ -491,7 +491,7 @@ class ShellToolMiddleware(AgentMiddleware[ShellToolState, Any]):
 
     def before_agent(self, state: ShellToolState, runtime: Runtime) -> dict[str, Any] | None:  # noqa: ARG002
         """Start the shell session and run startup commands."""
-        resources = self._create_resources()
+        resources = self._get_or_create_resources(state)
         return {"shell_session_resources": resources}
 
     async def abefore_agent(self, state: ShellToolState, runtime: Runtime) -> dict[str, Any] | None:
@@ -500,7 +500,10 @@ class ShellToolMiddleware(AgentMiddleware[ShellToolState, Any]):
 
     def after_agent(self, state: ShellToolState, runtime: Runtime) -> None:  # noqa: ARG002
         """Run shutdown commands and release resources when an agent completes."""
-        resources = self._ensure_resources(state)
+        resources = state.get("shell_session_resources")
+        if not isinstance(resources, _SessionResources):
+            # Resources were never created, nothing to clean up
+            return
         try:
             self._run_shutdown_commands(resources.session)
         finally:
@@ -510,17 +513,26 @@ class ShellToolMiddleware(AgentMiddleware[ShellToolState, Any]):
         """Async run shutdown commands and release resources when an agent completes."""
         return self.after_agent(state, runtime)
 
-    def _ensure_resources(self, state: ShellToolState) -> _SessionResources:
+    def _get_or_create_resources(self, state: ShellToolState) -> _SessionResources:
+        """Get existing resources from state or create new ones if they don't exist.
+
+        This method enables resumability by checking if resources already exist in the state
+        (e.g., after an interrupt), and only creating new resources if they're not present.
+
+        Args:
+            state: The agent state which may contain shell session resources.
+
+        Returns:
+            Session resources, either retrieved from state or newly created.
+        """
         resources = state.get("shell_session_resources")
-        if resources is not None and not isinstance(resources, _SessionResources):
-            resources = None
-        if resources is None:
-            msg = (
-                "Shell session resources are unavailable. Ensure `before_agent` ran successfully "
-                "before invoking the shell tool."
-            )
-            raise ToolException(msg)
-        return resources
+        if isinstance(resources, _SessionResources):
+            return resources
+
+        new_resources = self._create_resources()
+        # Cast needed to make state dict-like for mutation
+        cast("dict[str, Any]", state)["shell_session_resources"] = new_resources
+        return new_resources
 
     def _create_resources(self) -> _SessionResources:
         workspace = self._workspace_root

libs/langchain_v1/langchain/agents/middleware/summarization.py

@@ -1,8 +1,10 @@
 """Summarization middleware."""
 
 import uuid
-from collections.abc import Callable, Iterable
-from typing import Any, cast
+import warnings
+from collections.abc import Callable, Iterable, Mapping
+from functools import cache, partial
+from typing import Any, Literal, cast
 
 from langchain_core.messages import (
     AIMessage,
@@ -51,13 +53,81 @@ Messages to summarize:
 {messages}
 </messages>"""  # noqa: E501
 
-SUMMARY_PREFIX = "## Previous conversation summary:"
-
 _DEFAULT_MESSAGES_TO_KEEP = 20
 _DEFAULT_TRIM_TOKEN_LIMIT = 4000
 _DEFAULT_FALLBACK_MESSAGE_COUNT = 15
 _SEARCH_RANGE_FOR_TOOL_PAIRS = 5
 
+ContextFraction = tuple[Literal["fraction"], float]
+"""Fraction of model's maximum input tokens.
+
+Example:
+    To specify 50% of the model's max input tokens:
+
+    ```python
+    ("fraction", 0.5)
+    ```
+"""
+
+ContextTokens = tuple[Literal["tokens"], int]
+"""Absolute number of tokens.
+
+Example:
+    To specify 3000 tokens:
+
+    ```python
+    ("tokens", 3000)
+    ```
+"""
+
+ContextMessages = tuple[Literal["messages"], int]
+"""Absolute number of messages.
+
+Example:
+    To specify 50 messages:
+
+    ```python
+    ("messages", 50)
+    ```
+"""
+
+ContextSize = ContextFraction | ContextTokens | ContextMessages
+"""Union type for context size specifications.
+
+Can be either:
+
+- [`ContextFraction`][langchain.agents.middleware.summarization.ContextFraction]: A
+    fraction of the model's maximum input tokens.
+- [`ContextTokens`][langchain.agents.middleware.summarization.ContextTokens]: An absolute
+    number of tokens.
+- [`ContextMessages`][langchain.agents.middleware.summarization.ContextMessages]: An
+    absolute number of messages.
+
+Depending on use with `trigger` or `keep` parameters, this type indicates either
+when to trigger summarization or how much context to retain.
+
+Example:
+    ```python
+    # ContextFraction
+    context_size: ContextSize = ("fraction", 0.5)
+
+    # ContextTokens
+    context_size: ContextSize = ("tokens", 3000)
+
+    # ContextMessages
+    context_size: ContextSize = ("messages", 50)
+    ```
+"""
+
+
+def _get_approximate_token_counter(model: BaseChatModel) -> TokenCounter:
+    """Tune parameters of approximate token counter based on model type."""
+    if model._llm_type == "anthropic-chat":
+        # 3.3 was estimated in an offline experiment, comparing with Claude's token-counting
+        # API: https://platform.claude.com/docs/en/build-with-claude/token-counting
+        return partial(count_tokens_approximately, chars_per_token=3.3)
+    return count_tokens_approximately
+
 
 class SummarizationMiddleware(AgentMiddleware):
     """Summarizes conversation history when token limits are approached.
@@ -70,34 +140,129 @@ class SummarizationMiddleware(AgentMiddleware):
     def __init__(
         self,
         model: str | BaseChatModel,
-        max_tokens_before_summary: int | None = None,
-        messages_to_keep: int = _DEFAULT_MESSAGES_TO_KEEP,
+        *,
+        trigger: ContextSize | list[ContextSize] | None = None,
+        keep: ContextSize = ("messages", _DEFAULT_MESSAGES_TO_KEEP),
         token_counter: TokenCounter = count_tokens_approximately,
         summary_prompt: str = DEFAULT_SUMMARY_PROMPT,
-        summary_prefix: str = SUMMARY_PREFIX,
+        trim_tokens_to_summarize: int | None = _DEFAULT_TRIM_TOKEN_LIMIT,
+        **deprecated_kwargs: Any,
     ) -> None:
         """Initialize summarization middleware.
 
         Args:
             model: The language model to use for generating summaries.
-            max_tokens_before_summary: Token threshold to trigger summarization.
-                If `None`, summarization is disabled.
-            messages_to_keep: Number of recent messages to preserve after summarization.
+            trigger: One or more thresholds that trigger summarization.
+
+                Provide a single
+                [`ContextSize`][langchain.agents.middleware.summarization.ContextSize]
+                tuple or a list of tuples, in which case summarization runs when any
+                threshold is met.
+
+                !!! example
+
+                    ```python
+                    # Trigger summarization when 50 messages is reached
+                    ("messages", 50)
+
+                    # Trigger summarization when 3000 tokens is reached
+                    ("tokens", 3000)
+
+                    # Trigger summarization either when 80% of model's max input tokens
+                    # is reached or when 100 messages is reached (whichever comes first)
+                    [("fraction", 0.8), ("messages", 100)]
+                    ```
+
+                    See [`ContextSize`][langchain.agents.middleware.summarization.ContextSize]
+                    for more details.
+            keep: Context retention policy applied after summarization.
+
+                Provide a [`ContextSize`][langchain.agents.middleware.summarization.ContextSize]
+                tuple to specify how much history to preserve.
+
+                Defaults to keeping the most recent `20` messages.
+
+                Does not support multiple values like `trigger`.
+
+                !!! example
+
+                    ```python
+                    # Keep the most recent 20 messages
+                    ("messages", 20)
+
+                    # Keep the most recent 3000 tokens
+                    ("tokens", 3000)
+
+                    # Keep the most recent 30% of the model's max input tokens
+                    ("fraction", 0.3)
+                    ```
             token_counter: Function to count tokens in messages.
             summary_prompt: Prompt template for generating summaries.
-            summary_prefix: Prefix added to system message when including summary.
+            trim_tokens_to_summarize: Maximum tokens to keep when preparing messages for
+                the summarization call.
+
+                Pass `None` to skip trimming entirely.
         """
+        # Handle deprecated parameters
+        if "max_tokens_before_summary" in deprecated_kwargs:
+            value = deprecated_kwargs["max_tokens_before_summary"]
+            warnings.warn(
+                "max_tokens_before_summary is deprecated. Use trigger=('tokens', value) instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if trigger is None and value is not None:
+                trigger = ("tokens", value)
+
+        if "messages_to_keep" in deprecated_kwargs:
+            value = deprecated_kwargs["messages_to_keep"]
+            warnings.warn(
+                "messages_to_keep is deprecated. Use keep=('messages', value) instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if keep == ("messages", _DEFAULT_MESSAGES_TO_KEEP):
+                keep = ("messages", value)
+
         super().__init__()
 
         if isinstance(model, str):
             model = init_chat_model(model)
 
         self.model = model
-        self.max_tokens_before_summary = max_tokens_before_summary
-        self.messages_to_keep = messages_to_keep
-        self.token_counter = token_counter
+        if trigger is None:
+            self.trigger: ContextSize | list[ContextSize] | None = None
+            trigger_conditions: list[ContextSize] = []
+        elif isinstance(trigger, list):
+            validated_list = [self._validate_context_size(item, "trigger") for item in trigger]
+            self.trigger = validated_list
+            trigger_conditions = validated_list
+        else:
+            validated = self._validate_context_size(trigger, "trigger")
+            self.trigger = validated
+            trigger_conditions = [validated]
+        self._trigger_conditions = trigger_conditions
+
+        self.keep = self._validate_context_size(keep, "keep")
+        if token_counter is count_tokens_approximately:
+            self.token_counter = _get_approximate_token_counter(self.model)
+        else:
+            self.token_counter = token_counter
         self.summary_prompt = summary_prompt
-        self.summary_prefix = summary_prefix
+        self.trim_tokens_to_summarize = trim_tokens_to_summarize
+
+        requires_profile = any(condition[0] == "fraction" for condition in self._trigger_conditions)
+        if self.keep[0] == "fraction":
+            requires_profile = True
+        if requires_profile and self._get_profile_limits() is None:
+            msg = (
+                "Model profile information is required to use fractional token limits, "
+                "and is unavailable for the specified model. Please use absolute token "
+                "counts instead, or pass "
+                '`\n\nChatModel(..., profile={"max_input_tokens": ...})`.\n\n'
+                "with a desired integer value of the model's maximum input tokens."
+            )
+            raise ValueError(msg)
 
     def before_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:  # noqa: ARG002
         """Process messages before model invocation, potentially triggering summarization."""
@@ -105,13 +270,10 @@ class SummarizationMiddleware(AgentMiddleware):
         self._ensure_message_ids(messages)
 
         total_tokens = self.token_counter(messages)
-        if (
-            self.max_tokens_before_summary is not None
-            and total_tokens < self.max_tokens_before_summary
-        ):
+        if not self._should_summarize(messages, total_tokens):
             return None
 
-        cutoff_index = self._find_safe_cutoff(messages)
+        cutoff_index = self._determine_cutoff_index(messages)
 
         if cutoff_index <= 0:
             return None
@@ -129,6 +291,158 @@ class SummarizationMiddleware(AgentMiddleware):
             ]
         }
 
+    async def abefore_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:  # noqa: ARG002
+        """Process messages before model invocation, potentially triggering summarization."""
+        messages = state["messages"]
+        self._ensure_message_ids(messages)
+
+        total_tokens = self.token_counter(messages)
+        if not self._should_summarize(messages, total_tokens):
+            return None
+
+        cutoff_index = self._determine_cutoff_index(messages)
+
+        if cutoff_index <= 0:
+            return None
+
+        messages_to_summarize, preserved_messages = self._partition_messages(messages, cutoff_index)
+
+        summary = await self._acreate_summary(messages_to_summarize)
+        new_messages = self._build_new_messages(summary)
+
+        return {
+            "messages": [
+                RemoveMessage(id=REMOVE_ALL_MESSAGES),
+                *new_messages,
+                *preserved_messages,
+            ]
+        }
+
+    def _should_summarize(self, messages: list[AnyMessage], total_tokens: int) -> bool:
+        """Determine whether summarization should run for the current token usage."""
+        if not self._trigger_conditions:
+            return False
+
+        for kind, value in self._trigger_conditions:
+            if kind == "messages" and len(messages) >= value:
+                return True
+            if kind == "tokens" and total_tokens >= value:
+                return True
+            if kind == "fraction":
+                max_input_tokens = self._get_profile_limits()
+                if max_input_tokens is None:
+                    continue
+                threshold = int(max_input_tokens * value)
+                if threshold <= 0:
+                    threshold = 1
+                if total_tokens >= threshold:
+                    return True
+        return False
+
+    def _determine_cutoff_index(self, messages: list[AnyMessage]) -> int:
+        """Choose cutoff index respecting retention configuration."""
+        kind, value = self.keep
+        if kind in {"tokens", "fraction"}:
+            token_based_cutoff = self._find_token_based_cutoff(messages)
+            if token_based_cutoff is not None:
+                return token_based_cutoff
+            # None cutoff -> model profile data not available (caught in __init__ but
+            # here for safety), fallback to message count
+            return self._find_safe_cutoff(messages, _DEFAULT_MESSAGES_TO_KEEP)
+        return self._find_safe_cutoff(messages, cast("int", value))
+
+    def _find_token_based_cutoff(self, messages: list[AnyMessage]) -> int | None:
+        """Find cutoff index based on target token retention."""
+        if not messages:
+            return 0
+
+        @cache
+        def suffix_token_count(start_index: int) -> int:
+            return self.token_counter(messages[start_index:])
+
+        kind, value = self.keep
+        if kind == "fraction":
+            max_input_tokens = self._get_profile_limits()
+            if max_input_tokens is None:
+                return None
+            target_token_count = int(max_input_tokens * value)
+        elif kind == "tokens":
+            target_token_count = int(value)
+        else:
+            return None
+
+        if target_token_count <= 0:
+            target_token_count = 1
+
+        if suffix_token_count(0) <= target_token_count:
+            return 0
+
+        # Use binary search to identify the earliest message index that keeps the
+        # suffix within the token budget.
+        left, right = 0, len(messages)
+        cutoff_candidate = len(messages)
+        max_iterations = len(messages).bit_length() + 1
+        for _ in range(max_iterations):
+            if left >= right:
+                break
+
+            mid = (left + right) // 2
+            if suffix_token_count(mid) <= target_token_count:
+                cutoff_candidate = mid
+                right = mid
+            else:
+                left = mid + 1
+
+        if cutoff_candidate == len(messages):
+            cutoff_candidate = left
+
+        if cutoff_candidate >= len(messages):
+            if len(messages) == 1:
+                return 0
+            cutoff_candidate = len(messages) - 1
+
+        for i in range(cutoff_candidate, len(messages) + 1):
+            if (
+                self._is_safe_cutoff_point(messages, i)
+                and suffix_token_count(i) <= target_token_count
+            ):
+                return i
+
+        return 0
+
+    def _get_profile_limits(self) -> int | None:
+        """Retrieve max input token limit from the model profile."""
+        try:
+            profile = self.model.profile
+        except AttributeError:
+            return None
+
+        if not isinstance(profile, Mapping):
+            return None
+
+        max_input_tokens = profile.get("max_input_tokens")
+
+        if not isinstance(max_input_tokens, int):
+            return None
+
+        return max_input_tokens
+
+    def _validate_context_size(self, context: ContextSize, parameter_name: str) -> ContextSize:
+        """Validate context configuration tuples."""
+        kind, value = context
+        if kind == "fraction":
+            if not 0 < value <= 1:
+                msg = f"Fractional {parameter_name} values must be between 0 and 1, got {value}."
+                raise ValueError(msg)
+        elif kind in {"tokens", "messages"}:
+            if value <= 0:
+                msg = f"{parameter_name} thresholds must be greater than 0, got {value}."
+                raise ValueError(msg)
+        else:
+            msg = f"Unsupported context size type {kind} for {parameter_name}."
+            raise ValueError(msg)
+        return context
+
     def _build_new_messages(self, summary: str) -> list[HumanMessage]:
         return [
             HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}")
@@ -151,16 +465,16 @@ class SummarizationMiddleware(AgentMiddleware):
 
         return messages_to_summarize, preserved_messages
 
-    def _find_safe_cutoff(self, messages: list[AnyMessage]) -> int:
+    def _find_safe_cutoff(self, messages: list[AnyMessage], messages_to_keep: int) -> int:
         """Find safe cutoff point that preserves AI/Tool message pairs.
 
         Returns the index where messages can be safely cut without separating
-        related AI and Tool messages. Returns 0 if no safe cutoff is found.
+        related AI and Tool messages. Returns `0` if no safe cutoff is found.
         """
-        if len(messages) <= self.messages_to_keep:
+        if len(messages) <= messages_to_keep:
             return 0
 
-        target_cutoff = len(messages) - self.messages_to_keep
+        target_cutoff = len(messages) - messages_to_keep
 
         for i in range(target_cutoff, -1, -1):
             if self._is_safe_cutoff_point(messages, i):
@@ -229,16 +543,35 @@ class SummarizationMiddleware(AgentMiddleware):
 
         try:
             response = self.model.invoke(self.summary_prompt.format(messages=trimmed_messages))
-            return cast("str", response.content).strip()
+            return response.text.strip()
+        except Exception as e:  # noqa: BLE001
+            return f"Error generating summary: {e!s}"
+
+    async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary for the given messages."""
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed_messages:
+            return "Previous conversation was too long to summarize."
+
+        try:
+            response = await self.model.ainvoke(
+                self.summary_prompt.format(messages=trimmed_messages)
+            )
+            return response.text.strip()
         except Exception as e:  # noqa: BLE001
             return f"Error generating summary: {e!s}"
 
     def _trim_messages_for_summary(self, messages: list[AnyMessage]) -> list[AnyMessage]:
         """Trim messages to fit within summary generation limits."""
         try:
+            if self.trim_tokens_to_summarize is None:
+                return messages
             return trim_messages(
                 messages,
-                max_tokens=_DEFAULT_TRIM_TOKEN_LIMIT,
+                max_tokens=self.trim_tokens_to_summarize,
                 token_counter=self.token_counter,
                 start_on="human",
                 strategy="last",